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Saitta S, Carioni M, Mukherjee S, Schönlieb CB, Redaelli A. Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI. Comput Methods Programs Biomed 2024; 246:108057. [PMID: 38335865 DOI: 10.1016/j.cmpb.2024.108057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 02/02/2024] [Accepted: 02/02/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND AND OBJECTIVE 4D flow magnetic resonance imaging provides time-resolved blood flow velocity measurements, but suffers from limitations in spatio-temporal resolution and noise. In this study, we investigated the use of sinusoidal representation networks (SIRENs) to improve denoising and super-resolution of velocity fields measured by 4D flow MRI in the thoracic aorta. METHODS Efficient training of SIRENs in 4D was achieved by sampling voxel coordinates and enforcing the no-slip condition at the vessel wall. A set of synthetic measurements were generated from computational fluid dynamics simulations, reproducing different noise levels. The influence of SIREN architecture was systematically investigated, and the performance of our method was compared to existing approaches for 4D flow denoising and super-resolution. RESULTS Compared to existing techniques, a SIREN with 300 neurons per layer and 20 layers achieved lower errors (up to 50% lower vector normalized root mean square error, 42% lower magnitude normalized root mean square error, and 15% lower direction error) in velocity and wall shear stress fields. Applied to real 4D flow velocity measurements in a patient-specific aortic aneurysm, our method produced denoised and super-resolved velocity fields while maintaining accurate macroscopic flow measurements. CONCLUSIONS This study demonstrates the feasibility of using SIRENs for complex blood flow velocity representation from clinical 4D flow, with quick execution and straightforward implementation.
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Affiliation(s)
- Simone Saitta
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
| | - Marcello Carioni
- Department of Applied Mathematics, University of Twente, 7500AE Enschede, the Netherlands
| | - Subhadip Mukherjee
- Department of Electronics & Electrical Communication Engineering, Indian Institute of Technology (IIT) Kharagpur, India
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Alberto Redaelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
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2
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Huang J, Ferreira PF, Wang L, Wu Y, Aviles-Rivero AI, Schönlieb CB, Scott AD, Khalique Z, Dwornik M, Rajakulasingam R, De Silva R, Pennell DJ, Nielles-Vallespin S, Yang G. Deep learning-based diffusion tensor cardiac magnetic resonance reconstruction: a comparison study. Sci Rep 2024; 14:5658. [PMID: 38454072 PMCID: PMC10920645 DOI: 10.1038/s41598-024-55880-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 02/27/2024] [Indexed: 03/09/2024] Open
Abstract
In vivo cardiac diffusion tensor imaging (cDTI) is a promising Magnetic Resonance Imaging (MRI) technique for evaluating the microstructure of myocardial tissue in living hearts, providing insights into cardiac function and enabling the development of innovative therapeutic strategies. However, the integration of cDTI into routine clinical practice poses challenging due to the technical obstacles involved in the acquisition, such as low signal-to-noise ratio and prolonged scanning times. In this study, we investigated and implemented three different types of deep learning-based MRI reconstruction models for cDTI reconstruction. We evaluated the performance of these models based on the reconstruction quality assessment, the diffusion tensor parameter assessment as well as the computational cost assessment. Our results indicate that the models discussed in this study can be applied for clinical use at an acceleration factor (AF) of × 2 and × 4 , with the D5C5 model showing superior fidelity for reconstruction and the SwinMR model providing higher perceptual scores. There is no statistical difference from the reference for all diffusion tensor parameters at AF × 2 or most DT parameters at AF × 4 , and the quality of most diffusion tensor parameter maps is visually acceptable. SwinMR is recommended as the optimal approach for reconstruction at AF × 2 and AF × 4 . However, we believe that the models discussed in this study are not yet ready for clinical use at a higher AF. At AF × 8 , the performance of all models discussed remains limited, with only half of the diffusion tensor parameters being recovered to a level with no statistical difference from the reference. Some diffusion tensor parameter maps even provide wrong and misleading information.
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Grants
- Wellcome Trust
- RG/19/1/34160 British Heart Foundation
- This study was supported in part by the UKRI Future Leaders Fellowship (MR/V023799/1), BHF (RG/19/1/34160), the ERC IMI (101005122), the H2020 (952172), the MRC (MC/PC/21013), the Royal Society (IEC/NSFC/211235), the NVIDIA Academic Hardware Grant Program, EPSRC (EP/V029428/1, EP/S026045/1, EP/T003553/1, EP/N014588/1, EP/T017961/1), and the Cambridge Mathematics of Information in Healthcare Hub (CMIH) Partnership Fund.
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Affiliation(s)
- Jiahao Huang
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK.
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK.
- Bioengineering Department and Imperial-X, Imperial College London, London, W12 7SL, UK.
| | - Pedro F Ferreira
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Lichao Wang
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Department of Computing, Imperial College London, London, UK
| | - Yinzhe Wu
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Angelica I Aviles-Rivero
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Andrew D Scott
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Zohya Khalique
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Maria Dwornik
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Ramyah Rajakulasingam
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Ranil De Silva
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Dudley J Pennell
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Sonia Nielles-Vallespin
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK.
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK.
- Bioengineering Department and Imperial-X, Imperial College London, London, W12 7SL, UK.
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Buddenkotte T, Rundo L, Woitek R, Escudero Sanchez L, Beer L, Crispin-Ortuzar M, Etmann C, Mukherjee S, Bura V, McCague C, Sahin H, Pintican R, Zerunian M, Allajbeu I, Singh N, Sahdev A, Havrilesky L, Cohn DE, Bateman NW, Conrads TP, Darcy KM, Maxwell GL, Freymann JB, Öktem O, Brenton JD, Sala E, Schönlieb CB. Deep learning-based segmentation of multisite disease in ovarian cancer. Eur Radiol Exp 2023; 7:77. [PMID: 38057616 PMCID: PMC10700248 DOI: 10.1186/s41747-023-00388-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 09/21/2023] [Indexed: 12/08/2023] Open
Abstract
PURPOSE To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods. METHODS A deep learning model for the two most common disease sites of high-grade serous ovarian cancer lesions (pelvis/ovaries and omentum) was developed and compared against the well-established "no-new-Net" framework and unrevised trainee radiologist segmentations. A total of 451 CT scans collected from four different institutions were used for training (n = 276), evaluation (n = 104) and testing (n = 71) of the methods. The performance was evaluated using the Dice similarity coefficient (DSC) and compared using a Wilcoxon test. RESULTS Our model outperformed no-new-Net for the pelvic/ovarian lesions in cross-validation, on the evaluation and test set by a significant margin (p values being 4 × 10-7, 3 × 10-4, 4 × 10-2, respectively), and for the omental lesions on the evaluation set (p = 1 × 10-3). Our model did not perform significantly differently in segmenting pelvic/ovarian lesions (p = 0.371) compared to a trainee radiologist. On an independent test set, the model achieved a DSC performance of 71 ± 20 (mean ± standard deviation) for pelvic/ovarian and 61 ± 24 for omental lesions. CONCLUSION Automated ovarian cancer segmentation on CT scans using deep neural networks is feasible and achieves performance close to a trainee-level radiologist for pelvic/ovarian lesions. RELEVANCE STATEMENT Automated segmentation of ovarian cancer may be used by clinicians for CT-based volumetric assessments and researchers for building complex analysis pipelines. KEY POINTS • The first automated approach for pelvic/ovarian and omental ovarian cancer lesion segmentation on CT images has been presented. • Automated segmentation of ovarian cancer lesions can be comparable with manual segmentation of trainee radiologists. • Careful hyperparameter tuning can provide models significantly outperforming strong state-of-the-art baselines.
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Affiliation(s)
- Thomas Buddenkotte
- Department, of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Department for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Hamburg, Germany
- jung diagnostics GmbH, Hamburg, Germany
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, Italy
| | - Ramona Woitek
- Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Medicine, Danube Private University, Krems, Austria
| | - Lorena Escudero Sanchez
- Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Lucian Beer
- Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria
| | - Mireia Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Christian Etmann
- Department, of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Subhadip Mukherjee
- Department, of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Vlad Bura
- Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca-Napoca, Romania
| | - Cathal McCague
- Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Hilal Sahin
- Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Radiology, Tepecik Training and Research Hospital, Izmir, Turkey
| | - Roxana Pintican
- Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca-Napoca, Romania
- Department of Radiology, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca-Napoca, Romania
| | - Marta Zerunian
- Department of Medical-Surgical and Translational Medicine-Radiology Unit, Sapienza University of Rome, Sant'Andrea Hospital, Rome, Italy
| | - Iris Allajbeu
- Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Naveena Singh
- Department of Clinical Pathology, Barts Health NHS Trust, London, UK
| | - Anju Sahdev
- Department of Radiology, Barts Health NHS Trust, London, UK
| | | | - David E Cohn
- Departmant of Obstetrics and Gynecology, Division of Gynecologic Oncology, Ohio State University Comprehensive Cancer Center, Ohio State University College of Medicine, Columbus, OH, USA
| | - Nicholas W Bateman
- Department of Obstetrics and Gynecology, Gynecologic Cancer Center of Excellence, Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Uniformed Services University, Bethesda, MD, USA
| | - Thomas P Conrads
- Department of Obstetrics and Gynecology, Gynecologic Cancer Center of Excellence, Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Uniformed Services University, Bethesda, MD, USA
- Department of Obstetrics and Gynecology, Inova Fairfax Medical Campus, Falls Church, VA, USA
- Inova Center for Personalized Health, Inova Schar Cancer Institute, Falls Church, VA, USA
| | - Kathleen M Darcy
- Department of Obstetrics and Gynecology, Gynecologic Cancer Center of Excellence, Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Uniformed Services University, Bethesda, MD, USA
| | - G Larry Maxwell
- Department of Obstetrics and Gynecology, Gynecologic Cancer Center of Excellence, Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Uniformed Services University, Bethesda, MD, USA
- Department of Obstetrics and Gynecology, Inova Fairfax Medical Campus, Falls Church, VA, USA
| | - John B Freymann
- Cancer Imaging Informatics Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Ozan Öktem
- Department of Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden
| | - James D Brenton
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Evis Sala
- Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.
- Dipartimento Di Scienze Radiologiche Ed Ematologiche, Universita Cattolica del Sacro Cuore, Rome, Italy.
- Dipartimento Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia, Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
| | - Carola-Bibiane Schönlieb
- Department, of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
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4
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Shadbahr T, Roberts M, Stanczuk J, Gilbey J, Teare P, Dittmer S, Thorpe M, Torné RV, Sala E, Lió P, Patel M, Preller J, Rudd JHF, Mirtti T, Rannikko AS, Aston JAD, Tang J, Schönlieb CB. The impact of imputation quality on machine learning classifiers for datasets with missing values. Commun Med (Lond) 2023; 3:139. [PMID: 37803172 PMCID: PMC10558448 DOI: 10.1038/s43856-023-00356-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 09/13/2023] [Indexed: 10/08/2023] Open
Abstract
BACKGROUND Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete samples. The focus of the machine learning researcher is to optimise the classifier's performance. METHODS We utilise three simulated and three real-world clinical datasets with different feature types and missingness patterns. Initially, we evaluate how the downstream classifier performance depends on the choice of classifier and imputation methods. We employ ANOVA to quantitatively evaluate how the choice of missingness rate, imputation method, and classifier method influences the performance. Additionally, we compare commonly used methods for assessing imputation quality and introduce a class of discrepancy scores based on the sliced Wasserstein distance. We also assess the stability of the imputations and the interpretability of model built on the imputed data. RESULTS The performance of the classifier is most affected by the percentage of missingness in the test data, with a considerable performance decline observed as the test missingness rate increases. We also show that the commonly used measures for assessing imputation quality tend to lead to imputed data which poorly matches the underlying data distribution, whereas our new class of discrepancy scores performs much better on this measure. Furthermore, we show that the interpretability of classifier models trained using poorly imputed data is compromised. CONCLUSIONS It is imperative to consider the quality of the imputation when performing downstream classification as the effects on the classifier can be considerable.
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Affiliation(s)
- Tolou Shadbahr
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Michael Roberts
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.
- Data Science & Artificial Intelligence, AstraZeneca, Cambridge, UK.
| | - Jan Stanczuk
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Julian Gilbey
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Philip Teare
- Data Science & Artificial Intelligence, AstraZeneca, Cambridge, UK
| | - Sören Dittmer
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
- ZeTeM, University of Bremen, Bremen, Germany
| | - Matthew Thorpe
- Department of Mathematics, University of Manchester, Manchester, UK
| | - Ramon Viñas Torné
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Pietro Lió
- Department of Mathematics, University of Manchester, Manchester, UK
| | - Mishal Patel
- Data Science & Artificial Intelligence, AstraZeneca, Cambridge, UK
- Clinical Pharmacology & Safety Sciences, AstraZeneca, Cambridge, UK
| | - Jacobus Preller
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Trust, Cambridge, UK
| | - James H F Rudd
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Tuomas Mirtti
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- iCAN-Digital Precision Cancer Medicine Flagship, Helsinki, Finland
| | - Antti Sakari Rannikko
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- iCAN-Digital Precision Cancer Medicine Flagship, Helsinki, Finland
- Department of Urology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - John A D Aston
- Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, UK
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
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5
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Chen YK, Welsh S, Pillay AM, Tannenwald B, Bliznashki K, Hutchison E, Aston JAD, Schönlieb CB, Rudd JHF, Jones J, Roberts M. Common methodological pitfalls in ICI pneumonitis risk prediction studies. Front Immunol 2023; 14:1228812. [PMID: 37818359 PMCID: PMC10560723 DOI: 10.3389/fimmu.2023.1228812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 09/04/2023] [Indexed: 10/12/2023] Open
Abstract
Background Pneumonitis is one of the most common adverse events induced by the use of immune checkpoint inhibitors (ICI), accounting for a 20% of all ICI-associated deaths. Despite numerous efforts to identify risk factors and develop predictive models, there is no clinically deployed risk prediction model for patient risk stratification or for guiding subsequent monitoring. We believe this is due to systemic suboptimal approaches in study designs and methodologies in the literature. The nature and prevalence of different methodological approaches has not been thoroughly examined in prior systematic reviews. Methods The PubMed, medRxiv and bioRxiv databases were used to identify studies that aimed at risk factor discovery and/or risk prediction model development for ICI-induced pneumonitis (ICI pneumonitis). Studies were then analysed to identify common methodological pitfalls and their contribution to the risk of bias, assessed using the QUIPS and PROBAST tools. Results There were 51 manuscripts eligible for the review, with Japan-based studies over-represented, being nearly half (24/51) of all papers considered. Only 2/51 studies had a low risk of bias overall. Common bias-inducing practices included unclear diagnostic method or potential misdiagnosis, lack of multiple testing correction, the use of univariate analysis for selecting features for multivariable analysis, discretization of continuous variables, and inappropriate handling of missing values. Results from the risk model development studies were also likely to have been overoptimistic due to lack of holdout sets. Conclusions Studies with low risk of bias in their methodology are lacking in the existing literature. High-quality risk factor identification and risk model development studies are urgently required by the community to give the best chance of them progressing into a clinically deployable risk prediction model. Recommendations and alternative approaches for reducing the risk of bias were also discussed to guide future studies.
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Affiliation(s)
- Yichen K. Chen
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
| | - Sarah Welsh
- Department of Surgery, Cambridge University Hospitals, Cambridge, United Kingdom
| | - Ardon M. Pillay
- School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | | | - Kamen Bliznashki
- Digital Health, Oncology R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Emmette Hutchison
- Digital Health, Oncology R&D, AstraZeneca, Gaithersburg, MD, United States
| | - John A. D. Aston
- Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, United Kingdom
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
| | - James H. F. Rudd
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - James Jones
- Department of Oncology, Cambridge University Hospitals, Cambridge, United Kingdom
| | - Michael Roberts
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
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6
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Escudero Sanchez L, Buddenkotte T, Al Sa’d M, McCague C, Darcy J, Rundo L, Samoshkin A, Graves MJ, Hollamby V, Browne P, Crispin-Ortuzar M, Woitek R, Sala E, Schönlieb CB, Doran SJ, Öktem O. Integrating Artificial Intelligence Tools in the Clinical Research Setting: The Ovarian Cancer Use Case. Diagnostics (Basel) 2023; 13:2813. [PMID: 37685352 PMCID: PMC10486639 DOI: 10.3390/diagnostics13172813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 07/31/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
Artificial intelligence (AI) methods applied to healthcare problems have shown enormous potential to alleviate the burden of health services worldwide and to improve the accuracy and reproducibility of predictions. In particular, developments in computer vision are creating a paradigm shift in the analysis of radiological images, where AI tools are already capable of automatically detecting and precisely delineating tumours. However, such tools are generally developed in technical departments that continue to be siloed from where the real benefit would be achieved with their usage. Significant effort still needs to be made to make these advancements available, first in academic clinical research and ultimately in the clinical setting. In this paper, we demonstrate a prototype pipeline based entirely on open-source software and free of cost to bridge this gap, simplifying the integration of tools and models developed within the AI community into the clinical research setting, ensuring an accessible platform with visualisation applications that allow end-users such as radiologists to view and interact with the outcome of these AI tools.
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Affiliation(s)
- Lorena Escudero Sanchez
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, Li Ka Shing Centre, Cambridge CB2 0RE, UK
- National Cancer Imaging Translational Accelerator (NCITA) Consortium, UK
| | - Thomas Buddenkotte
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
- Department for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, 20246 Hamburg, Germany
- Jung Diagnostics GmbH, 22335 Hamburg, Germany
| | - Mohammad Al Sa’d
- National Cancer Imaging Translational Accelerator (NCITA) Consortium, UK
- Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College, London SW7 2AZ, UK
| | - Cathal McCague
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, Li Ka Shing Centre, Cambridge CB2 0RE, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
| | - James Darcy
- National Cancer Imaging Translational Accelerator (NCITA) Consortium, UK
- Division of Radiotherapy and Imaging, Institute of Cancer Research, London SW7 3RP, UK
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, Li Ka Shing Centre, Cambridge CB2 0RE, UK
- Department of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, 84084 Fisciano, Italy
| | - Alex Samoshkin
- Office for Translational Research, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0SP, UK
| | - Martin J. Graves
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
| | - Victoria Hollamby
- Research and Information Governance, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0SP, UK
| | - Paul Browne
- High Performance Computing Department, University of Cambridge, Cambridge CB3 0RB, UK
| | - Mireia Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, Li Ka Shing Centre, Cambridge CB2 0RE, UK
- Department of Oncology, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Ramona Woitek
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, Li Ka Shing Centre, Cambridge CB2 0RE, UK
- Research Centre for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, 3500 Krems, Austria
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, Li Ka Shing Centre, Cambridge CB2 0RE, UK
- National Cancer Imaging Translational Accelerator (NCITA) Consortium, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
- Dipartimento di Scienze Radiologiche ed Ematologiche, Universita Cattolica del Sacro Cuore, 00168 Rome, Italy
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
| | - Simon J. Doran
- National Cancer Imaging Translational Accelerator (NCITA) Consortium, UK
- Division of Radiotherapy and Imaging, Institute of Cancer Research, London SW7 3RP, UK
| | - Ozan Öktem
- Department of Mathematics, KTH-Royal Institute of Technology, SE-100 44 Stockholm, Sweden
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7
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Grossmann TG, Schönlieb CB, Da Rold O. Extracting chain lines and laid lines from digital images of medieval paper using spectral total variation decomposition. Herit Sci 2023; 11:180. [PMID: 37638147 PMCID: PMC10447590 DOI: 10.1186/s40494-023-01013-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 07/29/2023] [Indexed: 08/29/2023]
Abstract
Medieval paper, a handmade product, is made with a mould which leaves an indelible imprint on the sheet of paper. This imprint includes chain lines, laid lines and watermarks which are often visible on the sheet. Extracting these features allows the identification of the paper stock and gives information about the chronology, localisation and movement of manuscripts and people. Most computational work for feature extraction of paper analysis has so far focused on radiography or transmitted light images. While these imaging methods provide clear visualisation of the features of interest, they are expensive and time consuming in their acquisition and not feasible for smaller institutions. However, reflected light images of medieval paper manuscripts are abundant and possibly cheaper in their acquisition. In this paper, we propose algorithms to detect and extract the laid and chain lines from reflected light images. We tackle the main drawback of reflected light images, that is, the low contrast attenuation of chain and laid lines and intensity jumps due to noise and degradation, by employing the spectral total variation decomposition and develop methods for subsequent chain and laid line extraction. Our results clearly demonstrate the feasibility of using reflected light images in paper analysis. This work enables feature extraction for paper manuscripts that have otherwise not been analysed due to a lack of appropriate images. We also open the door for paper stock identification at scale.
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Affiliation(s)
- Tamara G. Grossmann
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
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8
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Breger A, Selby I, Roberts M, Babar J, Gkrania-Klotsas E, Preller J, Escudero Sánchez L, Rudd JHF, Aston JAD, Weir-McCall JR, Sala E, Schönlieb CB. A pipeline to further enhance quality, integrity and reusability of the NCCID clinical data. Sci Data 2023; 10:493. [PMID: 37500661 PMCID: PMC10374610 DOI: 10.1038/s41597-023-02340-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 06/27/2023] [Indexed: 07/29/2023] Open
Abstract
The National COVID-19 Chest Imaging Database (NCCID) is a centralized UK database of thoracic imaging and corresponding clinical data. It is made available by the National Health Service Artificial Intelligence (NHS AI) Lab to support the development of machine learning tools focused on Coronavirus Disease 2019 (COVID-19). A bespoke cleaning pipeline for NCCID, developed by the NHSx, was introduced in 2021. We present an extension to the original cleaning pipeline for the clinical data of the database. It has been adjusted to correct additional systematic inconsistencies in the raw data such as patient sex, oxygen levels and date values. The most important changes will be discussed in this paper, whilst the code and further explanations are made publicly available on GitLab. The suggested cleaning will allow global users to work with more consistent data for the development of machine learning tools without being an expert. In addition, it highlights some of the challenges when working with clinical multi-center data and includes recommendations for similar future initiatives.
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Affiliation(s)
- Anna Breger
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.
- Center of Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.
| | - Ian Selby
- Department of Radiology, University of Cambridge, Cambridge, UK.
- Cambridge University Hospitals NHS Trust, Cambridge, UK.
| | - Michael Roberts
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Judith Babar
- Department of Radiology, University of Cambridge, Cambridge, UK
- Cambridge University Hospitals NHS Trust, Cambridge, UK
| | - Effrossyni Gkrania-Klotsas
- Cambridge University Hospitals NHS Trust, Cambridge, UK
- School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Jacobus Preller
- Cambridge University Hospitals NHS Trust, Cambridge, UK
- School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Lorena Escudero Sánchez
- Department of Radiology, University of Cambridge, Cambridge, UK
- Cancer Research UK (CRUK) Cambridge Centre, Cambridge, UK
| | - James H F Rudd
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - John A D Aston
- Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, UK
| | - Jonathan R Weir-McCall
- Department of Radiology, University of Cambridge, Cambridge, UK
- Cambridge University Hospitals NHS Trust, Cambridge, UK
- Department of Radiology, Royal Papworth Hospital, Cambridge, UK
| | - Evis Sala
- Advanced Radiodiagnostics Centre, Fondazione Policlinico Universitario Agostino Gemelli, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
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9
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Sabaté Landman M, Biguri A, Hatamikia S, Boardman R, Aston J, Schönlieb CB. On Krylov methods for large-scale CBCT reconstruction. Phys Med Biol 2023. [PMID: 37192631 DOI: 10.1088/1361-6560/acd616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Krylov subspace methods are a powerful family of iterative solvers for linear systems of equations, which are commonly used for inverse problems due to their intrinsic regularization properties. Moreover, these methods are naturally suited to solve large-scale problems, as they only require matrix-vector products with the system matrix (and its adjoint) to compute approximate solutions, and they display a very fast convergence. 
Even if this class of methods has been widely researched and studied in the numerical linear algebra community, its use in applied medical physics and applied engineering is still very limited. e.g. in realistic large-scale Computed Tomography (CT) problems, and more specifically in Cone Beam CT (CBCT). This work attempts to breach this gap by providing a general framework for the most relevant Krylov subspace methods applied to 3D CT problems, including the most well-known Krylov solvers for non-square systems (CGLS, LSQR, LSMR), possibly in combination with Tikhonov regularization, and methods that incorporate total variation (TV) regularization. This is provided within an open source framework: the Tomographic Iterative GPU-based Reconstruction (TIGRE) toolbox, with the idea of promoting accessibility and reproducibility of the results for the algorithms presented. Finally, numerical results in synthetic and real-world 3D CT applications (medical CBCT and μ-CT datasets) are provided to showcase and compare the different Krylov subspace methods presented in the paper, as well as their suitability for different kinds of problems.
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Affiliation(s)
- Malena Sabaté Landman
- Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, Wilberforce Rd, Cambridge, Cambridgeshire, CB3 0WA, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Ander Biguri
- Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, Wilberforce Rd, Cambridge, Cambridgeshire, CB3 0WA, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Sepideh Hatamikia
- Austrian Center for Medical Innovation and Technology, Viktor Kaplan-Straße 2/1, Wiener Neustadt, 2700, AUSTRIA
| | - Richard Boardman
- University of Southampton, University Rd, Southampton, Hampshire, SO17 1BJ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - John Aston
- Department of Pure Mathematics and Mathematical Statistics (DPMMS), University of Cambridge, Wilberforce Rd, Cambridge, Cambridgeshire, CB2 1TN, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Carola-Bibiane Schönlieb
- DAMTP, University of Cambridge, Office: F0.06, Wilberforce Road, Cambridge, CB3 0WA, Cambridge, Cambridgeshire, CB2 1TN, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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10
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Yeung M, Rundo L, Nan Y, Sala E, Schönlieb CB, Yang G. Calibrating the Dice Loss to Handle Neural Network Overconfidence for Biomedical Image Segmentation. J Digit Imaging 2023; 36:739-752. [PMID: 36474089 PMCID: PMC10039156 DOI: 10.1007/s10278-022-00735-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 10/30/2022] [Accepted: 10/31/2022] [Indexed: 12/12/2022] Open
Abstract
The Dice similarity coefficient (DSC) is both a widely used metric and loss function for biomedical image segmentation due to its robustness to class imbalance. However, it is well known that the DSC loss is poorly calibrated, resulting in overconfident predictions that cannot be usefully interpreted in biomedical and clinical practice. Performance is often the only metric used to evaluate segmentations produced by deep neural networks, and calibration is often neglected. However, calibration is important for translation into biomedical and clinical practice, providing crucial contextual information to model predictions for interpretation by scientists and clinicians. In this study, we provide a simple yet effective extension of the DSC loss, named the DSC++ loss, that selectively modulates the penalty associated with overconfident, incorrect predictions. As a standalone loss function, the DSC++ loss achieves significantly improved calibration over the conventional DSC loss across six well-validated open-source biomedical imaging datasets, including both 2D binary and 3D multi-class segmentation tasks. Similarly, we observe significantly improved calibration when integrating the DSC++ loss into four DSC-based loss functions. Finally, we use softmax thresholding to illustrate that well calibrated outputs enable tailoring of recall-precision bias, which is an important post-processing technique to adapt the model predictions to suit the biomedical or clinical task. The DSC++ loss overcomes the major limitation of the DSC loss, providing a suitable loss function for training deep learning segmentation models for use in biomedical and clinical practice. Source code is available at https://github.com/mlyg/DicePlusPlus .
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Affiliation(s)
- Michael Yeung
- Department of Radiology, University of Cambridge, Hills Rd, Cambridge, CB2 0QQ UK
- National Heart & Lung Institute, Imperial College London, Dovehouse St, London, SW3 6LY UK
- Department of Computing, Imperial College London, London, UK
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge, Hills Rd, Cambridge, CB2 0QQ UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Robinson Way, Cambridge, CB2 0RE UK
- Department of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, Fisciano, Salerno 84084 Italy
| | - Yang Nan
- National Heart & Lung Institute, Imperial College London, Dovehouse St, London, SW3 6LY UK
| | - Evis Sala
- Department of Radiology, University of Cambridge, Hills Rd, Cambridge, CB2 0QQ UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Robinson Way, Cambridge, CB2 0RE UK
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Rd, Cambridge, CB3 0WA UK
| | - Guang Yang
- National Heart & Lung Institute, Imperial College London, Dovehouse St, London, SW3 6LY UK
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11
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Wei T, Aviles-Rivero AI, Wang S, Huang Y, Gilbert FJ, Schönlieb CB, Chen CW. Beyond fine-tuning: Classifying high resolution mammograms using function-preserving transformations. Med Image Anal 2022; 82:102618. [PMID: 36183607 DOI: 10.1016/j.media.2022.102618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 08/03/2022] [Accepted: 09/02/2022] [Indexed: 11/15/2022]
Abstract
The task of classifying mammograms is very challenging because the lesion is usually small in the high resolution image. The current state-of-the-art approaches for medical image classification rely on using the de-facto method for convolutional neural networks-fine-tuning. However, there are fundamental differences between natural images and medical images, which based on existing evidence from the literature, limits the overall performance gain when designed with algorithmic approaches. In this paper, we propose to go beyond fine-tuning by introducing a novel framework called MorphHR, in which we highlight a new transfer learning scheme. The idea behind the proposed framework is to integrate function-preserving transformations, for any continuous non-linear activation neurons, to internally regularise the network for improving mammograms classification. The proposed solution offers two major advantages over the existing techniques. Firstly and unlike fine-tuning, the proposed approach allows for modifying not only the last few layers but also several of the first ones on a deep ConvNet. By doing this, we can design the network front to be suitable for learning domain specific features. Secondly, the proposed scheme is scalable to hardware. Therefore, one can fit high resolution images on standard GPU memory. We show that by using high resolution images, one prevents losing relevant information. We demonstrate, through numerical and visual experiments, that the proposed approach yields to a significant improvement in the classification performance over state-of-the-art techniques, and is indeed on a par with radiology experts. Moreover and for generalisation purposes, we show the effectiveness of the proposed learning scheme on another large dataset, the ChestX-ray14, surpassing current state-of-the-art techniques.
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Affiliation(s)
- Tao Wei
- The Department of Computer Science, State University of New York at Buffalo, NY, USA
| | | | - Shuo Wang
- The Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China; Shanghai Key Laboratory of MICCAI, Shanghai, China
| | - Yuan Huang
- The Department of Radiology, University of Cambridge, UK
| | | | | | - Chang Wen Chen
- The Department of Computer Science, State University of New York at Buffalo, NY, USA
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12
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van Gogh S, Mukherjee S, Xu J, Wang Z, Rawlik M, Varga Z, Alaifari R, Schönlieb CB, Stampanoni M. Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior. PLoS One 2022; 17:e0272963. [PMID: 36048759 PMCID: PMC9436132 DOI: 10.1371/journal.pone.0272963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 07/31/2022] [Indexed: 11/21/2022] Open
Abstract
Breast cancer remains the most prevalent malignancy in women in many countries around the world, thus calling for better imaging technologies to improve screening and diagnosis. Grating interferometry (GI)-based phase contrast X-ray CT is a promising technique which could make the transition to clinical practice and improve breast cancer diagnosis by combining the high three-dimensional resolution of conventional CT with higher soft-tissue contrast. Unfortunately though, obtaining high-quality images is challenging. Grating fabrication defects and photon starvation lead to high noise amplitudes in the measured data. Moreover, the highly ill-conditioned differential nature of the GI-CT forward operator renders the inversion from corrupted data even more cumbersome. In this paper, we propose a novel regularized iterative reconstruction algorithm with an improved tomographic operator and a powerful data-driven regularizer to tackle this challenging inverse problem. Our algorithm combines the L-BFGS optimization scheme with a data-driven prior parameterized by a deep neural network. Importantly, we propose a novel regularization strategy to ensure that the trained network is non-expansive, which is critical for the convergence and stability analysis we provide. We empirically show that the proposed method achieves high quality images, both on simulated data as well as on real measurements.
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Affiliation(s)
- Stefano van Gogh
- Department of Electrical Engineering and Information Technology, ETH Zürich and University of Zürich, Zürich, Switzerland
- Photon Science Division, Paul Scherrer Institut, Villigen, Switzerland
- * E-mail:
| | - Subhadip Mukherjee
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
| | - Jinqiu Xu
- Department of Electrical Engineering and Information Technology, ETH Zürich and University of Zürich, Zürich, Switzerland
- Photon Science Division, Paul Scherrer Institut, Villigen, Switzerland
| | - Zhentian Wang
- Department of Engineering Physics, Tsinghua University, Beijing, China
- Key Laboratory of Particle and Radiation Imaging of Ministry of Education, Tsinghua University, Beijing, China
| | - Michał Rawlik
- Department of Electrical Engineering and Information Technology, ETH Zürich and University of Zürich, Zürich, Switzerland
- Photon Science Division, Paul Scherrer Institut, Villigen, Switzerland
| | - Zsuzsanna Varga
- Institute of Pathology and Molecular Pathology, University Hospital Zürich, Zürich, Switzerland
| | - Rima Alaifari
- Department of Mathematics, ETH Zürich, Zürich, Switzerland
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
| | - Marco Stampanoni
- Department of Electrical Engineering and Information Technology, ETH Zürich and University of Zürich, Zürich, Switzerland
- Photon Science Division, Paul Scherrer Institut, Villigen, Switzerland
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13
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Toader B, Boulanger J, Korolev Y, Lenz MO, Manton J, Schönlieb CB, Mureşan L. Image Reconstruction in Light-Sheet Microscopy: Spatially Varying Deconvolution and Mixed Noise. J Math Imaging Vis 2022; 64:968-992. [PMID: 36329880 PMCID: PMC7613773 DOI: 10.1007/s10851-022-01100-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Accepted: 04/23/2022] [Indexed: 06/16/2023]
Abstract
We study the problem of deconvolution for light-sheet microscopy, where the data is corrupted by spatially varying blur and a combination of Poisson and Gaussian noise. The spatial variation of the point spread function of a light-sheet microscope is determined by the interaction between the excitation sheet and the detection objective PSF. We introduce a model of the image formation process that incorporates this interaction and we formulate a variational model that accounts for the combination of Poisson and Gaussian noise through a data fidelity term consisting of the infimal convolution of the single noise fidelities, first introduced in L. Calatroni et al. (SIAM J Imaging Sci 10(3):1196-1233, 2017). We establish convergence rates and a discrepancy principle for the infimal convolution fidelity and the inverse problem is solved by applying the primal-dual hybrid gradient (PDHG) algorithm in a novel way. Numerical experiments performed on simulated and real data show superior reconstruction results in comparison with other methods.
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Affiliation(s)
- Bogdan Toader
- Cambridge Advanced Imaging Centre, University of Cambridge, Anatomy School, Downing Street, Cambridge, CB2 3DY UK
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA UK
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3DY UK
| | - Jérôme Boulanger
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB2 0QH UK
| | - Yury Korolev
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA UK
| | - Martin O. Lenz
- Cambridge Advanced Imaging Centre, University of Cambridge, Anatomy School, Downing Street, Cambridge, CB2 3DY UK
- Sainsbury Laboratory, University of Cambridge, 47 Bateman Street, Cambridge, CB2 1LR UK
| | - James Manton
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB2 0QH UK
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA UK
| | - Leila Mureşan
- Cambridge Advanced Imaging Centre, University of Cambridge, Anatomy School, Downing Street, Cambridge, CB2 3DY UK
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3DY UK
- Sainsbury Laboratory, University of Cambridge, 47 Bateman Street, Cambridge, CB2 1LR UK
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14
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Le E, Rundo L, Tarkin J, Evans N, Chowdhury M, Coughlin P, Pavey H, Wall C, Zaccagna F, Gallagher F, Huang Y, Sriranjan R, Le A, Weir-McCall J, Roberts M, Gilbert F, Warburton E, Schönlieb CB, Sala E, Rudd J. 146 Ct radiomics in carotid artery atherosclerosis: a systematic evaluation of robustness, reproducibility and predictive performance for culprit lesions. IMAGING 2022. [DOI: 10.1136/heartjnl-2022-bcs.146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
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15
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van Gogh S, Wang Z, Rawlik M, Etmann C, Mukherjee S, Schönlieb CB, Angst F, Boss A, Stampanoni M. INSIDEnet: Interpretable nonexpansive data-efficient network for denoising in grating interferometry breast CT. Med Phys 2022; 49:3729-3748. [PMID: 35257395 PMCID: PMC9311686 DOI: 10.1002/mp.15595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/25/2021] [Accepted: 01/07/2022] [Indexed: 11/11/2022] Open
Abstract
PURPOSE Breast cancer is the most common malignancy in women. Unfortunately, current breast imaging techniques all suffer from certain limitations: they are either not fully three-dimensional, have insufficient resolution or low soft-tissue contrast. Grating Interferometry Breast Computed Tomography (GI-BCT) is a promising X-ray phase contrast modality that could overcome these limitations by offering high soft-tissue contrast and excellent 3D resolution. To enable the transition of this technology to clinical practice, dedicated data processing algorithms must be developed in order to effectively retrieve the signals of interest from the measured raw data. METHODS This article proposes a novel denoising algorithm which can cope with the high noise amplitudes and heteroscedasticity which arise in GI-BCT when operated in a low-dose regime to effectively regularize the ill-conditioned GI-BCT inverse problem. We present a data-driven algorithm called INSIDEnet which combines different ideas such as multiscale image processing, transform-domain filtering, transform learning and explicit orthogonality to build an Interpretable NonexpanSIve Data-Efficient network (INSIDEnet). RESULTS We apply the method to simulated breast phantom datasets and to real data acquired on a GI-BCT prototype and show that the proposed algorithm outperforms traditional state-of-the-art filters and is competitive with deep neural networks. The strong inductive bias given by the proposed model's architecture allows to reliably train the algorithm with very limited data while providing high model interpretability, thus offering a great advantage over classical convolutional neural networks (CNNs). CONCLUSIONS The proposed INSIDEnet is highly data-efficient, interpretable and outperforms state-of-the-art CNNs when trained on very limited training data. We expect the proposed method to become an important tool as part of a dedicated Plug-and-Play GI-BCT reconstruction framework, needed to translate this promising technology to the clinics.
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Affiliation(s)
- Stefano van Gogh
- Paul Scherrer Institute, Photon Science Division, X-ray tomography group, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland.,ETH Zürich, Department for Electrical Engineering and Information Technology, X-ray tomography group, Gloriastrasse 35, Zürich, 8092, Switzerland
| | - Zhentian Wang
- Tsinghua University, Department of Engineering Physics, Haidian District, Beijing, 100084, China.,Key Laboratory of Particle & Radiation Imaging (Tsinghua University) of Ministry of Education, Haidian District, Beijing, 100084, China
| | - Michał Rawlik
- Paul Scherrer Institute, Photon Science Division, X-ray tomography group, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland.,ETH Zürich, Department for Electrical Engineering and Information Technology, X-ray tomography group, Gloriastrasse 35, Zürich, 8092, Switzerland
| | - Christian Etmann
- University of Cambridge, Cambridge Image Analysis group, Centre for Mathematical Sciences, Wilberforce Road, Cambridge, CB3 0WA, United Kingdom
| | - Subhadip Mukherjee
- University of Cambridge, Cambridge Image Analysis group, Centre for Mathematical Sciences, Wilberforce Road, Cambridge, CB3 0WA, United Kingdom
| | - Carola-Bibiane Schönlieb
- University of Cambridge, Cambridge Image Analysis group, Centre for Mathematical Sciences, Wilberforce Road, Cambridge, CB3 0WA, United Kingdom
| | - Florian Angst
- University Hospital Zürich, Institute for diagnostic and interventional Radiology, Rämistrasse 100, Zürich, 8091, Switzerland
| | - Andreas Boss
- University Hospital Zürich, Institute for diagnostic and interventional Radiology, Rämistrasse 100, Zürich, 8091, Switzerland
| | - Marco Stampanoni
- Paul Scherrer Institute, Photon Science Division, X-ray tomography group, Forschungsstrasse 111, Villigen PSI, 5232, Switzerland.,ETH Zürich, Department for Electrical Engineering and Information Technology, X-ray tomography group, Gloriastrasse 35, Zürich, 8092, Switzerland
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16
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Ehrhardt MJ, Gallagher FA, McLean MA, Schönlieb CB. Enhancing the spatial resolution of hyperpolarized carbon-13 MRI of human brain metabolism using structure guidance. Magn Reson Med 2022; 87:1301-1312. [PMID: 34687088 DOI: 10.1002/mrm.29045] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 09/24/2021] [Accepted: 09/27/2021] [Indexed: 12/14/2022]
Abstract
PURPOSE Dynamic nuclear polarization is an emerging imaging method that allows noninvasive investigation of tissue metabolism. However, the relatively low metabolic spatial resolution that can be achieved limits some applications, and improving this resolution could have important implications for the technique. METHODS We propose to enhance the 3D resolution of carbon-13 magnetic resonance imaging (13 C-MRI) using the structural information provided by hydrogen-1 MRI (1 H-MRI). The proposed approach relies on variational regularization in 3D with a directional total variation regularizer, resulting in a convex optimization problem which is robust with respect to the parameters and can efficiently be solved by many standard optimization algorithms. Validation was carried out using an in silico phantom, an in vitro phantom and in vivo data from four human volunteers. RESULTS The clinical data used in this study were upsampled by a factor of 4 in-plane and by a factor of 15 out-of-plane, thereby revealing occult information. A key finding is that 3D super-resolution shows superior performance compared to several 2D super-resolution approaches: for example, for the in silico data, the mean-squared-error was reduced by around 40% and for all data produced increased anatomical definition of the metabolic imaging. CONCLUSION The proposed approach generates images with enhanced anatomical resolution while largely preserving the quantitative measurements of metabolism. Although the work requires clinical validation against tissue measures of metabolism, it offers great potential in the field of 13 C-MRI and could significantly improve image quality in the future.
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Affiliation(s)
- Matthias J Ehrhardt
- Department of Mathematical Sciences, University of Bath, Bath, UK
- Institute for Mathematical Innovation, University of Bath, Bath, UK
| | | | - Mary A McLean
- Department of Radiology, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Carola-Bibiane Schönlieb
- Department for Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
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17
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Aviles-Rivero AI, Sellars P, Schönlieb CB, Papadakis N. GraphXCOVID: Explainable deep graph diffusion pseudo-Labelling for identifying COVID-19 on chest X-rays. Pattern Recognit 2022; 122:108274. [PMID: 34462610 PMCID: PMC8387569 DOI: 10.1016/j.patcog.2021.108274] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 08/20/2021] [Accepted: 08/21/2021] [Indexed: 05/07/2023]
Abstract
Can one learn to diagnose COVID-19 under extreme minimal supervision? Since the outbreak of the novel COVID-19 there has been a rush for developing automatic techniques for expert-level disease identification on Chest X-ray data. In particular, the use of deep supervised learning has become the go-to paradigm. However, the performance of such models is heavily dependent on the availability of a large and representative labelled dataset. The creation of which is a heavily expensive and time consuming task, and especially imposes a great challenge for a novel disease. Semi-supervised learning has shown the ability to match the incredible performance of supervised models whilst requiring a small fraction of the labelled examples. This makes the semi supervised paradigm an attractive option for identifying COVID-19. In this work, we introduce a graph based deep semi-supervised framework for classifying COVID-19 from chest X-rays. Our framework introduces an optimisation model for graph diffusion that reinforces the natural relation among the tiny labelled set and the vast unlabelled data. We then connect the diffusion prediction output as pseudo-labels that are used in an iterative scheme in a deep net. We demonstrate, through our experiments, that our model is able to outperform the current leading supervised model with a tiny fraction of the labelled examples. Finally, we provide attention maps to accommodate the radiologist's mental model, better fitting their perceptual and cognitive abilities. These visualisation aims to assist the radiologist in judging whether the diagnostic is correct or not, and in consequence to accelerate the decision.
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Affiliation(s)
| | - Philip Sellars
- DAMTP, Faculty of Mathematics, University of Cambridge, UK
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18
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Huang Y, Wang S, Luo T, Du MHF, Sun C, Sadat U, Schönlieb CB, Gillard JH, Zhang J, Teng Z. Estimation of the zero-pressure computational start shape of atherosclerotic plaques: Improving the backward displacement method with deformation gradient tensor. J Biomech 2022; 131:110910. [PMID: 34954525 DOI: 10.1016/j.jbiomech.2021.110910] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 10/16/2021] [Accepted: 12/10/2021] [Indexed: 01/06/2023]
Abstract
Advances in medical imaging have enabled patient-specific biomechanical modelling of arterial lesions such as atherosclerosis and aneurysm. Geometry acquired from in-vivo imaging is already pressurized and a zero-pressure computational start shape needs to be identified. The backward displacement algorithm was proposed to solve this inverse problem, utilizing fixed-point iterations to gradually approach the start shape. However, classical fixed-point implementations were reported with suboptimal convergence properties under large deformations. In this paper, a dynamic learning rate guided by the deformation gradient tensor was introduced to control the geometry update. The effectiveness of this new algorithm was demonstrated for both idealized and patient-specific models. The proposed algorithm led to faster convergence by accelerating the initial steps and helped to avoid the non-convergence in large-deformation problems.
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Affiliation(s)
- Yuan Huang
- EPSRC Cambridge Mathematics of Information in Healthcare, University of Cambridge, Cambridge, UK; Department of Radiology, University of Cambridge, Cambridge, UK
| | - Shuo Wang
- Department of Radiology, University of Cambridge, Cambridge, UK; Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Fudan University, Shanghai, China; Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China
| | - Tao Luo
- Department of Engineering, University of Cambridge, Cambridge, UK
| | - Michael Hong-Fei Du
- Department of Radiology, University of Cambridge, Cambridge, UK; John Farman Intensive Care Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Chang Sun
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Umar Sadat
- Cambridge Vascular Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Carola-Bibiane Schönlieb
- EPSRC Cambridge Mathematics of Information in Healthcare, University of Cambridge, Cambridge, UK; Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | | | - Jianjun Zhang
- Department of Radiology, Zhejiang Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhongzhao Teng
- Department of Radiology, University of Cambridge, Cambridge, UK; Nanjing Jingsan Medical Science and Technology, Ltd, Jiangsu, China.
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19
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Yeung M, Sala E, Schönlieb CB, Rundo L. Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Comput Med Imaging Graph 2021; 95:102026. [PMID: 34953431 PMCID: PMC8785124 DOI: 10.1016/j.compmedimag.2021.102026] [Citation(s) in RCA: 78] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 11/18/2021] [Accepted: 12/04/2021] [Indexed: 12/18/2022]
Abstract
Automatic segmentation methods are an important advancement in medical image analysis. Machine learning techniques, and deep neural networks in particular, are the state-of-the-art for most medical image segmentation tasks. Issues with class imbalance pose a significant challenge in medical datasets, with lesions often occupying a considerably smaller volume relative to the background. Loss functions used in the training of deep learning algorithms differ in their robustness to class imbalance, with direct consequences for model convergence. The most commonly used loss functions for segmentation are based on either the cross entropy loss, Dice loss or a combination of the two. We propose the Unified Focal loss, a new hierarchical framework that generalises Dice and cross entropy-based losses for handling class imbalance. We evaluate our proposed loss function on five publicly available, class imbalanced medical imaging datasets: CVC-ClinicDB, Digital Retinal Images for Vessel Extraction (DRIVE), Breast Ultrasound 2017 (BUS2017), Brain Tumour Segmentation 2020 (BraTS20) and Kidney Tumour Segmentation 2019 (KiTS19). We compare our loss function performance against six Dice or cross entropy-based loss functions, across 2D binary, 3D binary and 3D multiclass segmentation tasks, demonstrating that our proposed loss function is robust to class imbalance and consistently outperforms the other loss functions. Source code is available at: https://github.com/mlyg/unified-focal-loss. Loss function choice is crucial for class-imbalanced medical imaging datasets. Understanding the relationship between loss functions is key to inform choice. Unified Focal loss generalises Dice and cross-entropy based loss functions. Unified Focal loss outperforms various Dice and cross-entropy based loss functions.
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Affiliation(s)
- Michael Yeung
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom; School of Clinical Medicine, University of Cambridge, Cambridge CB2 0SP, United Kingdom.
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom.
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge CB3 0WA, United Kingdom.
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom; Department of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, Fisciano, SA 84084, Italy.
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20
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Bai X, Wang H, Ma L, Xu Y, Gan J, Fan Z, Yang F, Ma K, Yang J, Bai S, Shu C, Zou X, Huang R, Zhang C, Liu X, Tu D, Xu C, Zhang W, Wang X, Chen A, Zeng Y, Yang D, Wang MW, Holalkere N, Halin NJ, Kamel IR, Wu J, Peng X, Wang X, Shao J, Mongkolwat P, Zhang J, Liu W, Roberts M, Teng Z, Beer L, Escudero Sanchez L, Sala E, Rubin D, Weller A, Lasenby J, Zheng C, Wang J, Li Z, Schönlieb CB, Xia T. Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence. ArXiv 2021:arXiv:2111.09461v1. [PMID: 34815983 PMCID: PMC8609899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.
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Affiliation(s)
- Xiang Bai
- Department of Radiology, Tongji Hospital and Medical College, Huazhong University of Science and Technology, Wuhan, China
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
| | - Hanchen Wang
- Department of Engineering, University of Cambridge, Cambridge, UK
| | - Liya Ma
- Department of Radiology, Tongji Hospital and Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yongchao Xu
- Department of Radiology, Tongji Hospital and Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiefeng Gan
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
| | - Ziwei Fan
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
| | - Fan Yang
- Department of Radiology, Union Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ke Ma
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
| | - Jiehua Yang
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
| | - Song Bai
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
| | - Chang Shu
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
| | - Xinyu Zou
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
| | - Renhao Huang
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
| | | | - Xiaowu Liu
- HUST-HW Joint Innovation Lab, Wuhan, China
| | - Dandan Tu
- HUST-HW Joint Innovation Lab, Wuhan, China
| | - Chuou Xu
- Department of Radiology, Tongji Hospital and Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenqing Zhang
- Department of Radiology, Tongji Hospital and Medical College, Huazhong University of Science and Technology, Wuhan, China
| | | | | | | | - Dehua Yang
- The National Centre for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Ming-Wei Wang
- The National Centre for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Nagaraj Holalkere
- CardioVascular and Interventional Radiology, Radiology for Quality and Operations, The Cardiovascular Centre at Tufts Medical Centre, Radiology, Tufts University School of Medicine, Medford, USA
| | - Neil J. Halin
- CardioVascular and Interventional Radiology, Radiology for Quality and Operations, The Cardiovascular Centre at Tufts Medical Centre, Radiology, Tufts University School of Medicine, Medford, USA
| | - Ihab R. Kamel
- Russell H Morgan Department of Radiology & Radiologic Science, Johns Hopkins Hospital & Medicine Institute, Baltimore, USA
| | - Jia Wu
- Department of Radiation Oncology, School of Medicine, Stanford University, Palo Alto, USA
| | - Xuehua Peng
- Department of Radiology, Wuhan Central Hospital, Wuhan, China
| | - Xiang Wang
- Department of Radiology, Wuhan Children’s Hospital, Wuhan, China
| | - Jianbo Shao
- Department of Radiology, Wuhan Central Hospital, Wuhan, China
| | | | - Jianjun Zhang
- Thoracic/Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Centre, Houston, USA
- Translational Molecular Pathology, University of Texas MD Anderson Cancer Centre, Houston, USA
| | - Weiyang Liu
- Department of Engineering, University of Cambridge, Cambridge, UK
| | - Michael Roberts
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
- Oncology R&D at AstraZeneca, Cambridge, UK
| | - Zhongzhao Teng
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Lucian Beer
- Department of Radiology, University of Cambridge, Cambridge, UK
| | | | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Daniel Rubin
- Department of Biomedical Data Science, Radiology and Medicine, Stanford University, Palo Alto, USA
| | - Adrian Weller
- Department of Engineering, University of Cambridge, Cambridge, UK
- Alan Turing Institute, London, UK
| | - Joan Lasenby
- Department of Engineering, University of Cambridge, Cambridge, UK
| | - Chuangsheng Zheng
- Department of Radiology, Union Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jianming Wang
- Department of Hepatobiliary Pancreatic Surgery, Affiliated Tianyou Hospital, Wuhan University of Science and Technology, Wuhan, China
| | - Zhen Li
- Department of Radiology, Tongji Hospital and Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
- Alan Turing Institute, London, UK
| | - Tian Xia
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
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21
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Yeung M, Sala E, Schönlieb CB, Rundo L. Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy. Comput Biol Med 2021; 137:104815. [PMID: 34507156 PMCID: PMC8505797 DOI: 10.1016/j.compbiomed.2021.104815] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/26/2021] [Accepted: 08/26/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Colonoscopy remains the gold-standard screening for colorectal cancer. However, significant miss rates for polyps have been reported, particularly when there are multiple small adenomas. This presents an opportunity to leverage computer-aided systems to support clinicians and reduce the number of polyps missed. METHOD In this work we introduce the Focus U-Net, a novel dual attention-gated deep neural network, which combines efficient spatial and channel-based attention into a single Focus Gate module to encourage selective learning of polyp features. The Focus U-Net incorporates several further architectural modifications, including the addition of short-range skip connections and deep supervision. Furthermore, we introduce the Hybrid Focal loss, a new compound loss function based on the Focal loss and Focal Tversky loss, designed to handle class-imbalanced image segmentation. For our experiments, we selected five public datasets containing images of polyps obtained during optical colonoscopy: CVC-ClinicDB, Kvasir-SEG, CVC-ColonDB, ETIS-Larib PolypDB and EndoScene test set. We first perform a series of ablation studies and then evaluate the Focus U-Net on the CVC-ClinicDB and Kvasir-SEG datasets separately, and on a combined dataset of all five public datasets. To evaluate model performance, we use the Dice similarity coefficient (DSC) and Intersection over Union (IoU) metrics. RESULTS Our model achieves state-of-the-art results for both CVC-ClinicDB and Kvasir-SEG, with a mean DSC of 0.941 and 0.910, respectively. When evaluated on a combination of five public polyp datasets, our model similarly achieves state-of-the-art results with a mean DSC of 0.878 and mean IoU of 0.809, a 14% and 15% improvement over the previous state-of-the-art results of 0.768 and 0.702, respectively. CONCLUSIONS This study shows the potential for deep learning to provide fast and accurate polyp segmentation results for use during colonoscopy. The Focus U-Net may be adapted for future use in newer non-invasive colorectal cancer screening and more broadly to other biomedical image segmentation tasks similarly involving class imbalance and requiring efficiency.
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Affiliation(s)
- Michael Yeung
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, United Kingdom; School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SP, United Kingdom.
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, United Kingdom.
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, CB3 0WA, United Kingdom.
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, United Kingdom.
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22
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Wu J, Li C, Gensheimer M, Padda S, Kato F, Shirato H, Wei Y, Schönlieb CB, Price SJ, Jaffray D, Heymach J, Neal JW, Loo BW, Wakelee H, Diehn M, Li R. Radiological tumor classification across imaging modality and histology. NAT MACH INTELL 2021; 3:787-798. [PMID: 34841195 PMCID: PMC8612063 DOI: 10.1038/s42256-021-00377-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 06/24/2021] [Indexed: 02/07/2023]
Abstract
Radiomics refers to the high-throughput extraction of quantitative features from radiological scans and is widely used to search for imaging biomarkers for prediction of clinical outcomes. Current radiomic signatures suffer from limited reproducibility and generalizability, because most features are dependent on imaging modality and tumor histology, making them sensitive to variations in scan protocol. Here, we propose novel radiological features that are specially designed to ensure compatibility across diverse tissues and imaging contrast. These features provide systematic characterization of tumor morphology and spatial heterogeneity. In an international multi-institution study of 1,682 patients, we discover and validate four unifying imaging subtypes across three malignancies and two major imaging modalities. These tumor subtypes demonstrate distinct molecular characteristics and prognoses after conventional therapies. In advanced lung cancer treated with immunotherapy, one subtype is associated with improved survival and increased tumor-infiltrating lymphocytes compared with the others. Deep learning enables automatic tumor segmentation and reproducible subtype identification, which can facilitate practical implementation. The unifying radiological tumor classification may inform prognosis and treatment response for precision medicine.
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Affiliation(s)
- Jia Wu
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
- Department of Thoracic and Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Chao Li
- The Centre for Mathematical Imaging in Healthcare, Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, UK
- Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Michael Gensheimer
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Sukhmani Padda
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Fumi Kato
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan
| | - Hiroki Shirato
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Yiran Wei
- Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | | | - Stephen John Price
- Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - David Jaffray
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
- Office of the Chief Technology and Digital Officer, MD Anderson Cancer Center, Houston, TX, USA
| | - John Heymach
- Department of Thoracic and Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Joel W Neal
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Billy W Loo
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Heather Wakelee
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Maximilian Diehn
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA
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23
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van Eijnatten M, Rundo L, Batenburg KJ, Lucka F, Beddowes E, Caldas C, Gallagher FA, Sala E, Schönlieb CB, Woitek R. 3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning. Comput Methods Programs Biomed 2021; 208:106261. [PMID: 34289437 DOI: 10.1016/j.cmpb.2021.106261] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 06/24/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVES Deep learning is being increasingly used for deformable image registration and unsupervised approaches, in particular, have shown great potential. However, the registration of abdominopelvic Computed Tomography (CT) images remains challenging due to the larger displacements compared to those in brain or prostate Magnetic Resonance Imaging datasets that are typically considered as benchmarks. In this study, we investigate the use of the commonly used unsupervised deep learning framework VoxelMorph for the registration of a longitudinal abdominopelvic CT dataset acquired in patients with bone metastases from breast cancer. METHODS As a pre-processing step, the abdominopelvic CT images were refined by automatically removing the CT table and all other extra-corporeal components. To improve the learning capabilities of the VoxelMorph framework when only a limited amount of training data is available, a novel incremental training strategy is proposed based on simulated deformations of consecutive CT images in the longitudinal dataset. This devised training strategy was compared against training on simulated deformations of a single CT volume. A widely used software toolbox for deformable image registration called NiftyReg was used as a benchmark. The evaluations were performed by calculating the Dice Similarity Coefficient (DSC) between manual vertebrae segmentations and the Structural Similarity Index (SSIM). RESULTS The CT table removal procedure allowed both VoxelMorph and NiftyReg to achieve significantly better registration performance. In a 4-fold cross-validation scheme, the incremental training strategy resulted in better registration performance compared to training on a single volume, with a mean DSC of 0.929±0.037 and 0.883±0.033, and a mean SSIM of 0.984±0.009 and 0.969±0.007, respectively. Although our deformable image registration method did not outperform NiftyReg in terms of DSC (0.988±0.003) or SSIM (0.995±0.002), the registrations were approximately 300 times faster. CONCLUSIONS This study showed the feasibility of deep learning based deformable registration of longitudinal abdominopelvic CT images via a novel incremental training strategy based on simulated deformations.
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Affiliation(s)
- Maureen van Eijnatten
- Centrum Wiskunde & Informatica, 1098 XG Amsterdam, the Netherlands; Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, the Netherlands.
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge, CB2 0QQ Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, CB2 0RE Cambridge, United Kingdom
| | - K Joost Batenburg
- Centrum Wiskunde & Informatica, 1098 XG Amsterdam, the Netherlands; Mathematical Institute, Leiden University, 2300 RA Leiden, the Netherlands
| | - Felix Lucka
- Centrum Wiskunde & Informatica, 1098 XG Amsterdam, the Netherlands; Centre for Medical Image Computing, University College London, WC1E 6BT London, United Kingdom
| | - Emma Beddowes
- Cancer Research UK Cambridge Centre, University of Cambridge, CB2 0RE Cambridge, United Kingdom; Cancer Research UK Cambridge Institute, University of Cambridge, CB2 0RE Cambridge, United Kingdom; Department of Oncology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service (NHS) Foundation Trust, CB2 0QQ Cambridge, United Kingdom
| | - Carlos Caldas
- Cancer Research UK Cambridge Centre, University of Cambridge, CB2 0RE Cambridge, United Kingdom; Cancer Research UK Cambridge Institute, University of Cambridge, CB2 0RE Cambridge, United Kingdom; Department of Oncology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service (NHS) Foundation Trust, CB2 0QQ Cambridge, United Kingdom
| | - Ferdia A Gallagher
- Department of Radiology, University of Cambridge, CB2 0QQ Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, CB2 0RE Cambridge, United Kingdom
| | - Evis Sala
- Department of Radiology, University of Cambridge, CB2 0QQ Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, CB2 0RE Cambridge, United Kingdom
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, CB3 0WA Cambridge, United Kingdom
| | - Ramona Woitek
- Department of Radiology, University of Cambridge, CB2 0QQ Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, CB2 0RE Cambridge, United Kingdom; Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, 1090 Vienna, Austria
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24
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Celledoni E, Ehrhardt MJ, Etmann C, Owren B, Schönlieb CB, Sherry F. Equivariant neural networks for inverse problems. Inverse Probl 2021; 37:085006. [PMID: 34334869 PMCID: PMC8317019 DOI: 10.1088/1361-6420/ac104f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 06/08/2021] [Accepted: 06/30/2021] [Indexed: 06/13/2023]
Abstract
In recent years the use of convolutional layers to encode an inductive bias (translational equivariance) in neural networks has proven to be a very fruitful idea. The successes of this approach have motivated a line of research into incorporating other symmetries into deep learning methods, in the form of group equivariant convolutional neural networks. Much of this work has been focused on roto-translational symmetry of R d , but other examples are the scaling symmetry of R d and rotational symmetry of the sphere. In this work, we demonstrate that group equivariant convolutional operations can naturally be incorporated into learned reconstruction methods for inverse problems that are motivated by the variational regularisation approach. Indeed, if the regularisation functional is invariant under a group symmetry, the corresponding proximal operator will satisfy an equivariance property with respect to the same group symmetry. As a result of this observation, we design learned iterative methods in which the proximal operators are modelled as group equivariant convolutional neural networks. We use roto-translationally equivariant operations in the proposed methodology and apply it to the problems of low-dose computerised tomography reconstruction and subsampled magnetic resonance imaging reconstruction. The proposed methodology is demonstrated to improve the reconstruction quality of a learned reconstruction method with a little extra computational cost at training time but without any extra cost at test time.
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Affiliation(s)
- Elena Celledoni
- Department of Mathematical Sciences, NTNU, N-7491 Trondheim, Norway
| | - Matthias J Ehrhardt
- Institute for Mathematical Innovation, University of Bath, Bath BA2 7JU, United Kingdom
| | - Christian Etmann
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, United Kingdom
| | - Brynjulf Owren
- Department of Mathematical Sciences, NTNU, N-7491 Trondheim, Norway
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, United Kingdom
| | - Ferdia Sherry
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, United Kingdom
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Driggs D, Selby I, Roberts M, Gkrania-Klotsas E, Rudd JHF, Yang G, Babar J, Sala E, Schönlieb CB. Machine Learning for COVID-19 Diagnosis and Prognostication: Lessons for Amplifying the Signal While Reducing the Noise. Radiol Artif Intell 2021; 3:e210011. [PMID: 34240059 PMCID: PMC7995449 DOI: 10.1148/ryai.2021210011] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 03/01/2021] [Accepted: 03/03/2021] [Indexed: 12/19/2022]
Affiliation(s)
| | | | - Michael Roberts
- From the Department of Applied Mathematics and Theoretical Physics
(D.D., M.R., C.B.S.) and Division of Cardiovascular Medicine (J.H.F.R.),
University of Cambridge, Cambridge, England; Department of Radiology, School of
Clinical Medicine, University of Cambridge and CRUK Cambridge Centre, Cambridge
Biomedical Campus, Cambridge CB2 0QQ, England (I.S., J.B., E.S.);
Oncology R&D, AstraZeneca, Cambridge, England (M.R.); Department of
Infectious Diseases, University of Cambridge Hospitals, Cambridge, England
(E.G.K.); and National Heart and Lung Institute, Imperial College London,
London, England (G.Y.)
| | - Effrossyni Gkrania-Klotsas
- From the Department of Applied Mathematics and Theoretical Physics
(D.D., M.R., C.B.S.) and Division of Cardiovascular Medicine (J.H.F.R.),
University of Cambridge, Cambridge, England; Department of Radiology, School of
Clinical Medicine, University of Cambridge and CRUK Cambridge Centre, Cambridge
Biomedical Campus, Cambridge CB2 0QQ, England (I.S., J.B., E.S.);
Oncology R&D, AstraZeneca, Cambridge, England (M.R.); Department of
Infectious Diseases, University of Cambridge Hospitals, Cambridge, England
(E.G.K.); and National Heart and Lung Institute, Imperial College London,
London, England (G.Y.)
| | - James H. F. Rudd
- From the Department of Applied Mathematics and Theoretical Physics
(D.D., M.R., C.B.S.) and Division of Cardiovascular Medicine (J.H.F.R.),
University of Cambridge, Cambridge, England; Department of Radiology, School of
Clinical Medicine, University of Cambridge and CRUK Cambridge Centre, Cambridge
Biomedical Campus, Cambridge CB2 0QQ, England (I.S., J.B., E.S.);
Oncology R&D, AstraZeneca, Cambridge, England (M.R.); Department of
Infectious Diseases, University of Cambridge Hospitals, Cambridge, England
(E.G.K.); and National Heart and Lung Institute, Imperial College London,
London, England (G.Y.)
| | - Guang Yang
- From the Department of Applied Mathematics and Theoretical Physics
(D.D., M.R., C.B.S.) and Division of Cardiovascular Medicine (J.H.F.R.),
University of Cambridge, Cambridge, England; Department of Radiology, School of
Clinical Medicine, University of Cambridge and CRUK Cambridge Centre, Cambridge
Biomedical Campus, Cambridge CB2 0QQ, England (I.S., J.B., E.S.);
Oncology R&D, AstraZeneca, Cambridge, England (M.R.); Department of
Infectious Diseases, University of Cambridge Hospitals, Cambridge, England
(E.G.K.); and National Heart and Lung Institute, Imperial College London,
London, England (G.Y.)
| | - Judith Babar
- From the Department of Applied Mathematics and Theoretical Physics
(D.D., M.R., C.B.S.) and Division of Cardiovascular Medicine (J.H.F.R.),
University of Cambridge, Cambridge, England; Department of Radiology, School of
Clinical Medicine, University of Cambridge and CRUK Cambridge Centre, Cambridge
Biomedical Campus, Cambridge CB2 0QQ, England (I.S., J.B., E.S.);
Oncology R&D, AstraZeneca, Cambridge, England (M.R.); Department of
Infectious Diseases, University of Cambridge Hospitals, Cambridge, England
(E.G.K.); and National Heart and Lung Institute, Imperial College London,
London, England (G.Y.)
| | - Evis Sala
- From the Department of Applied Mathematics and Theoretical Physics
(D.D., M.R., C.B.S.) and Division of Cardiovascular Medicine (J.H.F.R.),
University of Cambridge, Cambridge, England; Department of Radiology, School of
Clinical Medicine, University of Cambridge and CRUK Cambridge Centre, Cambridge
Biomedical Campus, Cambridge CB2 0QQ, England (I.S., J.B., E.S.);
Oncology R&D, AstraZeneca, Cambridge, England (M.R.); Department of
Infectious Diseases, University of Cambridge Hospitals, Cambridge, England
(E.G.K.); and National Heart and Lung Institute, Imperial College London,
London, England (G.Y.)
| | - Carola-Bibiane Schönlieb
- From the Department of Applied Mathematics and Theoretical Physics
(D.D., M.R., C.B.S.) and Division of Cardiovascular Medicine (J.H.F.R.),
University of Cambridge, Cambridge, England; Department of Radiology, School of
Clinical Medicine, University of Cambridge and CRUK Cambridge Centre, Cambridge
Biomedical Campus, Cambridge CB2 0QQ, England (I.S., J.B., E.S.);
Oncology R&D, AstraZeneca, Cambridge, England (M.R.); Department of
Infectious Diseases, University of Cambridge Hospitals, Cambridge, England
(E.G.K.); and National Heart and Lung Institute, Imperial College London,
London, England (G.Y.)
| | - on behalf of the AIX-COVNET collaboration
- From the Department of Applied Mathematics and Theoretical Physics
(D.D., M.R., C.B.S.) and Division of Cardiovascular Medicine (J.H.F.R.),
University of Cambridge, Cambridge, England; Department of Radiology, School of
Clinical Medicine, University of Cambridge and CRUK Cambridge Centre, Cambridge
Biomedical Campus, Cambridge CB2 0QQ, England (I.S., J.B., E.S.);
Oncology R&D, AstraZeneca, Cambridge, England (M.R.); Department of
Infectious Diseases, University of Cambridge Hospitals, Cambridge, England
(E.G.K.); and National Heart and Lung Institute, Imperial College London,
London, England (G.Y.)
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26
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Wang G, Liu X, Shen J, Wang C, Li Z, Ye L, Wu X, Chen T, Wang K, Zhang X, Zhou Z, Yang J, Sang Y, Deng R, Liang W, Yu T, Gao M, Wang J, Yang Z, Cai H, Lu G, Zhang L, Yang L, Xu W, Wang W, Olvera A, Ziyar I, Zhang C, Li O, Liao W, Liu J, Chen W, Chen W, Shi J, Zheng L, Zhang L, Yan Z, Zou X, Lin G, Cao G, Lau LL, Mo L, Liang Y, Roberts M, Sala E, Schönlieb CB, Fok M, Lau JYN, Xu T, He J, Zhang K, Li W, Lin T. A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images. Nat Biomed Eng 2021; 5:509-521. [PMID: 33859385 PMCID: PMC7611049 DOI: 10.1038/s41551-021-00704-1] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 02/19/2021] [Indexed: 02/08/2023]
Abstract
Common lung diseases are first diagnosed using chest X-rays. Here, we show that a fully automated deep-learning pipeline for the standardization of chest X-ray images, for the visualization of lesions and for disease diagnosis can identify viral pneumonia caused by coronavirus disease 2019 (COVID-19) and assess its severity, and can also discriminate between viral pneumonia caused by COVID-19 and other types of pneumonia. The deep-learning system was developed using a heterogeneous multicentre dataset of 145,202 images, and tested retrospectively and prospectively with thousands of additional images across four patient cohorts and multiple countries. The system generalized across settings, discriminating between viral pneumonia, other types of pneumonia and the absence of disease with areas under the receiver operating characteristic curve (AUCs) of 0.94-0.98; between severe and non-severe COVID-19 with an AUC of 0.87; and between COVID-19 pneumonia and other viral or non-viral pneumonia with AUCs of 0.87-0.97. In an independent set of 440 chest X-rays, the system performed comparably to senior radiologists and improved the performance of junior radiologists. Automated deep-learning systems for the assessment of pneumonia could facilitate early intervention and provide support for clinical decision-making.
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Affiliation(s)
- Guangyu Wang
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
| | - Xiaohong Liu
- Department of Computer Science and Technology & BNRist, Tsinghua University, Beijing, China
| | - Jun Shen
- Department of Urology, Department of Radiology, Department of Emergency Medicine, Department of Disciplinary Development and Planning, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Chengdi Wang
- Department of Respiratory and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, Center for Translational Medicine and Innovations, West China Hospital, West China Medical School, Sichuan University, Chengdu, China
| | - Zhihuan Li
- Center for Biomedicine and Innovations, Faculty of Medicine, Macau University of Science and Technology and University Hospital, Macau, China
| | - Linsen Ye
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xingwang Wu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Ting Chen
- Department of Computer Science and Technology & BNRist, Tsinghua University, Beijing, China.
| | - Kai Wang
- Department of Computer Science and Technology & BNRist, Tsinghua University, Beijing, China
| | - Xuan Zhang
- Department of Computer Science and Technology & BNRist, Tsinghua University, Beijing, China
| | - Zhongguo Zhou
- The Sun Yat-sen Cancer Center, Sun Yat-sen University, Guangzhou, China
| | - Jian Yang
- The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
| | - Ye Sang
- The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
| | - Ruiyun Deng
- Department of Bioinformatics, Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Wenhua Liang
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China State Key Laboratory and National Clinical Research Center for Respiratory Disease, Guangzhou, China
| | - Tao Yu
- Department of Urology, Department of Radiology, Department of Emergency Medicine, Department of Disciplinary Development and Planning, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ming Gao
- Department of Urology, Department of Radiology, Department of Emergency Medicine, Department of Disciplinary Development and Planning, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jin Wang
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zehong Yang
- Department of Urology, Department of Radiology, Department of Emergency Medicine, Department of Disciplinary Development and Planning, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Huimin Cai
- Department of Bioinformatics, Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Guangming Lu
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Lingyan Zhang
- Department of Medical Imaging, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China
| | - Lei Yang
- Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Wenqin Xu
- Center for Biomedicine and Innovations, Faculty of Medicine, Macau University of Science and Technology and University Hospital, Macau, China
| | - Winston Wang
- Center for Biomedicine and Innovations, Faculty of Medicine, Macau University of Science and Technology and University Hospital, Macau, China
| | - Andrea Olvera
- Center for Biomedicine and Innovations, Faculty of Medicine, Macau University of Science and Technology and University Hospital, Macau, China
| | - Ian Ziyar
- Center for Biomedicine and Innovations, Faculty of Medicine, Macau University of Science and Technology and University Hospital, Macau, China
| | - Charlotte Zhang
- Department of Bioinformatics, Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Oulan Li
- Department of Bioinformatics, Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Weihua Liao
- Department of Medical Imaging and Deptartment of Cardiology, Xiangya Hospital, Central South University, Changsha, China
| | - Jun Liu
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, China
| | - Wen Chen
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Hubei, China
| | - Wei Chen
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jichan Shi
- Department of Infectious Disease, Wenzhou Central Hospital, Wenzhou, China
| | - Lianghong Zheng
- Center for Biomedicine and Innovations, Faculty of Medicine, Macau University of Science and Technology and University Hospital, Macau, China
| | - Longjiang Zhang
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Zhihan Yan
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiaoguang Zou
- Department of Respiratory and Critical Care Medicine, The First People's Hospital of Kashi Prefecture, Kashi, China
| | - Guiping Lin
- Department of Urology, Department of Radiology, Department of Emergency Medicine, Department of Disciplinary Development and Planning, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Guiqun Cao
- Department of Respiratory and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, Center for Translational Medicine and Innovations, West China Hospital, West China Medical School, Sichuan University, Chengdu, China
| | - Laurance L Lau
- Center for Biomedicine and Innovations, Faculty of Medicine, Macau University of Science and Technology and University Hospital, Macau, China
| | - Long Mo
- Department of Medical Imaging and Deptartment of Cardiology, Xiangya Hospital, Central South University, Changsha, China
| | - Yong Liang
- Center for Biomedicine and Innovations, Faculty of Medicine, Macau University of Science and Technology and University Hospital, Macau, China
| | - Michael Roberts
- Oncology R&D, AstraZeneca, Cambridge, UK
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Evis Sala
- Department of Radiology and Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Manson Fok
- Center for Biomedicine and Innovations, Faculty of Medicine, Macau University of Science and Technology and University Hospital, Macau, China
| | - Johnson Yiu-Nam Lau
- Department of Applied Biology and Chemical Technology, Hong Kong Polytechnic University, Hong Kong, China
| | - Tao Xu
- Department of Bioinformatics, Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Jianxing He
- Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China State Key Laboratory and National Clinical Research Center for Respiratory Disease, Guangzhou, China
| | - Kang Zhang
- Center for Biomedicine and Innovations, Faculty of Medicine, Macau University of Science and Technology and University Hospital, Macau, China.
- Department of Bioinformatics, Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China.
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, Center for Translational Medicine and Innovations, West China Hospital, West China Medical School, Sichuan University, Chengdu, China.
| | - Tianxin Lin
- Department of Urology, Department of Radiology, Department of Emergency Medicine, Department of Disciplinary Development and Planning, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
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27
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Mäki-Petäjä KM, McGeoch A, Yang LL, Hubsch A, McEniery CM, Meyer PAR, Mir F, Gajendragadkar P, Ramenatte N, Anandappa G, Franco SS, Bond SJ, Schönlieb CB, Boink Y, Brune C, Wilkinson IB, Jodrell DI, Cheriyan J. Mechanisms Underlying Vascular Endothelial Growth Factor Receptor Inhibition-Induced Hypertension: The HYPAZ Trial. Hypertension 2021; 77:1591-1599. [PMID: 33775123 PMCID: PMC7610566 DOI: 10.1161/hypertensionaha.120.16454] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 03/02/2021] [Indexed: 11/16/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Kaisa M Mäki-Petäjä
- Division of Experimental Medicine and Immunotherapeutics, University of Cambridge, U.K
| | - Adam McGeoch
- Division of Experimental Medicine and Immunotherapeutics, University of Cambridge, U.K
| | - Lucy L Yang
- Division of Experimental Medicine and Immunotherapeutics, University of Cambridge, U.K
| | - Annette Hubsch
- Division of Experimental Medicine and Immunotherapeutics, University of Cambridge, U.K
| | - Carmel M McEniery
- Division of Experimental Medicine and Immunotherapeutics, University of Cambridge, U.K
| | - Paul A R Meyer
- Division of Experimental Medicine and Immunotherapeutics, University of Cambridge, U.K
- Department of Medical Ophthalmology, Cambridge University Hospitals NHS Foundation Trust, U.K
| | - Fraz Mir
- Division of Experimental Medicine and Immunotherapeutics, University of Cambridge, U.K
| | - Parag Gajendragadkar
- Division of Experimental Medicine and Immunotherapeutics, University of Cambridge, U.K
| | - Nicola Ramenatte
- Cambridge Clinical Trials Unit, Cambridge University Hospitals NHS Foundation Trust, U.K
| | | | - Sara Santos Franco
- GlaxoSmithKline R&D Clinical Unit, Addenbrooke’s Hospital, Cambridge, U.K
| | - Simon J Bond
- Cambridge Clinical Trials Unit, Cambridge University Hospitals NHS Foundation Trust, U.K
| | | | - Yoeri Boink
- Department of Applied Mathematics, University of Twente, Netherlands
- Multi-Modality Medical Imaging group, Technical Medical Centre, University of Twente, Netherlands
| | - Christoph Brune
- Department of Applied Mathematics, University of Twente, Netherlands
| | - Ian B Wilkinson
- Division of Experimental Medicine and Immunotherapeutics, University of Cambridge, U.K
- Cambridge Clinical Trials Unit, Cambridge University Hospitals NHS Foundation Trust, U.K
| | - Duncan I. Jodrell
- Cambridge Clinical Trials Unit, Cambridge University Hospitals NHS Foundation Trust, U.K
- Department of Oncology, University of Cambridge, U.K
| | - Joseph Cheriyan
- Division of Experimental Medicine and Immunotherapeutics, University of Cambridge, U.K
- Cambridge Clinical Trials Unit, Cambridge University Hospitals NHS Foundation Trust, U.K
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28
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Le EPV, Rundo L, Tarkin JM, Evans NR, Chowdhury MM, Coughlin PA, Pavey H, Wall C, Zaccagna F, Gallagher FA, Huang Y, Sriranjan R, Le A, Weir-McCall JR, Roberts M, Gilbert FJ, Warburton EA, Schönlieb CB, Sala E, Rudd JHF. Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events. Sci Rep 2021; 11:3499. [PMID: 33568735 PMCID: PMC7876096 DOI: 10.1038/s41598-021-82760-w] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 01/21/2021] [Indexed: 02/02/2023] Open
Abstract
Radiomics, quantitative feature extraction from radiological images, can improve disease diagnosis and prognostication. However, radiomic features are susceptible to image acquisition and segmentation variability. Ideally, only features robust to these variations would be incorporated into predictive models, for good generalisability. We extracted 93 radiomic features from carotid artery computed tomography angiograms of 41 patients with cerebrovascular events. We tested feature robustness to region-of-interest perturbations, image pre-processing settings and quantisation methods using both single- and multi-slice approaches. We assessed the ability of the most robust features to identify culprit and non-culprit arteries using several machine learning algorithms and report the average area under the curve (AUC) from five-fold cross validation. Multi-slice features were superior to single for producing robust radiomic features (67 vs. 61). The optimal image quantisation method used bin widths of 25 or 30. Incorporating our top 10 non-redundant robust radiomics features into ElasticNet achieved an AUC of 0.73 and accuracy of 69% (compared to carotid calcification alone [AUC: 0.44, accuracy: 46%]). Our results provide key information for introducing carotid CT radiomics into clinical practice. If validated prospectively, our robust carotid radiomic set could improve stroke prediction and target therapies to those at highest risk.
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Affiliation(s)
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Jason M Tarkin
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Nicholas R Evans
- Department of Medicine, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Mohammed M Chowdhury
- Division of Vascular Surgery, Department of Surgery, University of Cambridge, Cambridge, UK
| | - Patrick A Coughlin
- Division of Vascular Surgery, Department of Surgery, University of Cambridge, Cambridge, UK
| | - Holly Pavey
- Division of Experimental Medicine and Immunotherapeutics, University of Cambridge, Cambridge, UK
| | - Chris Wall
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Fulvio Zaccagna
- Department of Radiology, University of Cambridge, Cambridge, UK
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | | | - Yuan Huang
- Department of Radiology, University of Cambridge, Cambridge, UK
- EPSRC Centre for Mathematical Imaging in Healthcare, University of Cambridge, Cambridge, UK
| | | | - Anthony Le
- School of Medicine, University of Leeds, Leeds, UK
| | | | - Michael Roberts
- EPSRC Centre for Mathematical Imaging in Healthcare, University of Cambridge, Cambridge, UK
- Oncology R&D, AstraZeneca, Cambridge, UK
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Fiona J Gilbert
- Department of Radiology, University of Cambridge, Cambridge, UK
| | | | - Carola-Bibiane Schönlieb
- EPSRC Centre for Mathematical Imaging in Healthcare, University of Cambridge, Cambridge, UK
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - James H F Rudd
- Department of Medicine, University of Cambridge, Cambridge, UK.
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29
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Kimanius D, Zickert G, Nakane T, Adler J, Lunz S, Schönlieb CB, Öktem O, Scheres SHW. Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination. IUCrJ 2021; 8:60-75. [PMID: 33520243 PMCID: PMC7793004 DOI: 10.1107/s2052252520014384] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 10/29/2020] [Indexed: 05/07/2023]
Abstract
Three-dimensional reconstruction of the electron-scattering potential of biological macromolecules from electron cryo-microscopy (cryo-EM) projection images is an ill-posed problem. The most popular cryo-EM software solutions to date rely on a regularization approach that is based on the prior assumption that the scattering potential varies smoothly over three-dimensional space. Although this approach has been hugely successful in recent years, the amount of prior knowledge that it exploits compares unfavorably with the knowledge about biological structures that has been accumulated over decades of research in structural biology. Here, a regularization framework for cryo-EM structure determination is presented that exploits prior knowledge about biological structures through a convolutional neural network that is trained on known macromolecular structures. This neural network is inserted into the iterative cryo-EM structure-determination process through an approach that is inspired by regularization by denoising. It is shown that the new regularization approach yields better reconstructions than the current state of the art for simulated data, and options to extend this work for application to experimental cryo-EM data are discussed.
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Affiliation(s)
- Dari Kimanius
- MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | - Gustav Zickert
- Department of Mathematics, Royal Institute of Technology (KTH), Sweden
| | - Takanori Nakane
- MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | | | - Sebastian Lunz
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
| | - Ozan Öktem
- Department of Mathematics, Royal Institute of Technology (KTH), Sweden
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30
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Corona V, Aviles-Rivero A, Debroux N, Le Guyader C, Schönlieb CB. Variational multi-task MRI reconstruction: Joint reconstruction, registration and super-resolution. Med Image Anal 2020; 68:101941. [PMID: 33385698 DOI: 10.1016/j.media.2020.101941] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 11/27/2020] [Accepted: 12/07/2020] [Indexed: 11/27/2022]
Abstract
Motion degradation is a central problem in Magnetic Resonance Imaging (MRI). This work addresses the problem of how to obtain higher quality, super-resolved motion-free reconstructions from highly undersampled MRI data. In this work, we present for the first time a variational multi-task framework that allows joining three relevant tasks in MRI: reconstruction, registration and super-resolution. Our framework takes a set of multiple undersampled MR acquisitions corrupted by motion into a novel multi-task optimisation model, which is composed of an L2 fidelity term that allows sharing representation between tasks, super-resolution foundations and hyperelastic deformations to model biological tissue behaviors. We demonstrate that this combination yields significant improvements over sequential models and other bi-task methods. Our results exhibit fine details and compensate for motion producing sharp and highly textured images compared to state of the art methods while keeping low CPU time. Our improvements are appraised on both clinical assessment and statistical analysis.
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Affiliation(s)
- Veronica Corona
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK.
| | | | - Noémie Debroux
- Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, F-63000 Clermont-Ferrand, France
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31
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Aviles-Rivero AI, Debroux N, Williams G, Graves MJ, Schönlieb CB. Compressed sensing plus motion (CS + M): A new perspective for improving undersampled MR image reconstruction. Med Image Anal 2020; 68:101933. [PMID: 33341495 DOI: 10.1016/j.media.2020.101933] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 11/23/2020] [Accepted: 11/27/2020] [Indexed: 10/22/2022]
Abstract
We address the problem of reconstructing high quality images from undersampled MRI data. This is a challenging task due to the highly ill-posed nature of the problem. In particular, in dynamic MRI scans, the interaction between the target structure and the physical motion affects the acquired measurements leading to blurring artefacts and loss of fine details. In this work, we propose a framework for dynamic MRI reconstruction framed under a new multi-task optimisation model called Compressed Sensing Plus Motion (CS + M). Firstly, we propose a single optimisation problem that simultaneously computes the MRI reconstruction and the physical motion. Secondly, we show our model can be efficiently solved by breaking it up into two computationally tractable problems. The potentials and generalisation capabilities of our approach are demonstrated in different clinical applications including cardiac cine, cardiac perfusion and brain perfusion imaging. We show, through numerical experiments, that the proposed scheme reduces blurring artefacts, and preserves the target shape and fine details in the reconstruction. We also report the highest quality reconstruction under high undersampling rates in comparison to several state of the art techniques.
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Affiliation(s)
| | - Noémie Debroux
- Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, France
| | - Guy Williams
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, UK
| | - Martin J Graves
- Department of Radiology, Cambridge University Hospitals, University of Cambridge, UK
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Liu J, Aviles-Rivero AI, Ji H, Schönlieb CB. Rethinking medical image reconstruction via shape prior, going deeper and faster: Deep joint indirect registration and reconstruction. Med Image Anal 2020; 68:101930. [PMID: 33378731 DOI: 10.1016/j.media.2020.101930] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 11/23/2020] [Accepted: 11/24/2020] [Indexed: 11/18/2022]
Abstract
Indirect image registration is a promising technique to improve image reconstruction quality by providing a shape prior for the reconstruction task. In this paper, we propose a novel hybrid method that seeks to reconstruct high quality images from few measurements whilst requiring low computational cost. With this purpose, our framework intertwines indirect registration and reconstruction tasks is a single functional. It is based on two major novelties. Firstly, we introduce a model based on deep nets to solve the indirect registration problem, in which the inversion and registration mappings are recurrently connected through a fixed-point interaction based sparse optimisation. Secondly, we introduce specific inversion blocks, that use the explicit physical forward operator, to map the acquired measurements to the image reconstruction. We also introduce registration blocks based deep nets to predict the registration parameters and warp transformation accurately and efficiently. We demonstrate, through extensive numerical and visual experiments, that our framework outperforms significantly classic reconstruction schemes and other bi-task method; this in terms of both image quality and computational time. Finally, we show generalisation capabilities of our approach by demonstrating their performance on fast Magnetic Resonance Imaging (MRI), sparse view computed tomography (CT) and low dose CT with measurements much below the Nyquist limit.
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Affiliation(s)
- Jiulong Liu
- Department of Mathematics, National University of Singapore, Singapore. https://github.com/jiulongliu/Deep-Joint-Indirect-Registration-and-Reconstruction
| | | | - Hui Ji
- Department of Mathematics, National University of Singapore, Singapore
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Bungert L, Burger M, Korolev Y, Schönlieb CB. Variational regularisation for inverse problems with imperfect forward operators and general noise models. Inverse Probl 2020; 36:125014. [PMID: 34149144 PMCID: PMC8208616 DOI: 10.1088/1361-6420/abc531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 10/23/2020] [Accepted: 10/27/2020] [Indexed: 06/12/2023]
Abstract
We study variational regularisation methods for inverse problems with imperfect forward operators whose errors can be modelled by order intervals in a partial order of a Banach lattice. We carry out analysis with respect to existence and convex duality for general data fidelity terms and regularisation functionals. Both for a priori and a posteriori parameter choice rules, we obtain convergence rates of the regularised solutions in terms of Bregman distances. Our results apply to fidelity terms such as Wasserstein distances, φ-divergences, norms, as well as sums and infimal convolutions of those.
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Affiliation(s)
- Leon Bungert
- Department Mathematik, University of Erlangen-Nürnberg, Cauerstrasse 11, 91058 Erlangen, Germany
- leon.bungert
| | - Martin Burger
- Department Mathematik, University of Erlangen-Nürnberg, Cauerstrasse 11, 91058 Erlangen, Germany
| | - Yury Korolev
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, United Kingdom
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, United Kingdom
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Le EPV, Evans N, Tarkin J, Chowdhury M, Zaccagna F, Pavey H, Wall C, Huang Y, Weir-McCall J, Warburton E, Rundo L, Schönlieb CB, Sala E, Rudd JHF. 105 Machine learning and carotid artery CT radiomics identify significant differences between culprit and non-culprit lesions in patients with stroke and transient ischaemic attack. Imaging 2020. [DOI: 10.1136/heartjnl-2020-bcs.105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023] Open
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Drechsler M, Lang LF, Al-Khatib L, Dirks H, Burger M, Schönlieb CB, Palacios IM. Optical flow analysis reveals that Kinesin-mediated advection impacts the orientation of microtubules in the Drosophila oocyte. Mol Biol Cell 2020; 31:1246-1258. [PMID: 32267197 PMCID: PMC7353148 DOI: 10.1091/mbc.e19-08-0440] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
The orientation of microtubule (MT) networks is exploited by motors to deliver cargoes to specific intracellular destinations and is thus essential for cell polarity and function. Reconstituted in vitro systems have largely contributed to understanding the molecular framework regulating the behavior of MT filaments. In cells, however, MTs are exposed to various biomechanical forces that might impact on their orientation, but little is known about it. Oocytes, which display forceful cytoplasmic streaming, are excellent model systems to study the impact of motion forces on cytoskeletons in vivo. Here we implement variational optical flow analysis as a new approach to analyze the polarity of MTs in the Drosophila oocyte, a cell that displays distinct Kinesin-dependent streaming. After validating the method as robust for describing MT orientation from confocal movies, we find that increasing the speed of flows results in aberrant plus end growth direction. Furthermore, we find that in oocytes where Kinesin is unable to induce cytoplasmic streaming, the growth direction of MT plus ends is also altered. These findings lead us to propose that cytoplasmic streaming - and thus motion by advection – contributes to the correct orientation of MTs in vivo. Finally, we propose a possible mechanism for a specialized cytoplasmic actin network (the actin mesh) to act as a regulator of flow speeds to counteract the recruitment of Kinesin to MTs.
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Affiliation(s)
- Maik Drechsler
- School of Biological and Chemical Sciences, Queen Mary University of London, London E1 4NS, UK.,Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, United Kingdom.,Department of Zoology and Developmental Biology, University of Osnabrück, 49076 Osnabrück, Germany
| | - Lukas F Lang
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge CB3 0WA, United Kingdom
| | - Layla Al-Khatib
- School of Biological and Chemical Sciences, Queen Mary University of London, London E1 4NS, UK
| | - Hendrik Dirks
- Institute for Computational and Applied Mathematics, University of Münster, 48149 Münster, Germany
| | - Martin Burger
- Department of Mathematics, Friedrich-Alexander Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge CB3 0WA, United Kingdom
| | - Isabel M Palacios
- School of Biological and Chemical Sciences, Queen Mary University of London, London E1 4NS, UK.,Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, United Kingdom
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Abstract
Uncompressed clinical data from modern positron emission tomography (PET) scanners are very large, exceeding 350 million data points (projection bins). The last decades have seen tremendous advancements in mathematical imaging tools many of which lead to non-smooth (i.e. non-differentiable) optimization problems which are much harder to solve than smooth optimization problems. Most of these tools have not been translated to clinical PET data, as the state-of-the-art algorithms for non-smooth problems do not scale well to large data. In this work, inspired by big data machine learning applications, we use advanced randomized optimization algorithms to solve the PET reconstruction problem for a very large class of non-smooth priors which includes for example total variation, total generalized variation, directional total variation and various different physical constraints. The proposed algorithm randomly uses subsets of the data and only updates the variables associated with these. While this idea often leads to divergent algorithms, we show that the proposed algorithm does indeed converge for any proper subset selection. Numerically, we show on real PET data (FDG and florbetapir) from a Siemens Biograph mMR that about ten projections and backprojections are sufficient to solve the MAP optimisation problem related to many popular non-smooth priors; thus showing that the proposed algorithm is fast enough to bring these models into routine clinical practice.
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Affiliation(s)
- Matthias J Ehrhardt
- Institute for Mathematical Innovation, University of Bath, Bath BA2 7JU, United Kingdom
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Li C, Wang S, Liu P, Torheim T, Boonzaier NR, van Dijken BR, Schönlieb CB, Markowetz F, Price SJ. Decoding the Interdependence of Multiparametric Magnetic Resonance Imaging to Reveal Patient Subgroups Correlated with Survivals. Neoplasia 2019; 21:442-449. [PMID: 30943446 PMCID: PMC6444075 DOI: 10.1016/j.neo.2019.03.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 03/11/2019] [Accepted: 03/12/2019] [Indexed: 11/29/2022]
Abstract
Glioblastoma is highly heterogeneous in microstructure and vasculature, creating various tumor microenvironments among patients, which may lead to different phenotypes. The purpose was to interrogate the interdependence of microstructure and vasculature using perfusion and diffusion imaging and to investigate the utility of this approach in tumor invasiveness assessment. A total of 115 primary glioblastoma patients were prospectively recruited for preoperative magnetic resonance imaging (MRI) and surgery. Apparent diffusion coefficient (ADC) was calculated from diffusion imaging, and relative cerebral blood volume (rCBV) was calculated from perfusion imaging. The empirical copula transform was applied to ADC and rCBV voxels in the contrast-enhancing tumor region to obtain their joint distribution, which was discretized to extract second-order features for an unsupervised hierarchical clustering. The lactate levels of patient subgroups, measured by MR spectroscopy, were compared. Survivals were analyzed using Kaplan-Meier and multivariate Cox regression analyses. The results showed that three patient subgroups were identified by the unsupervised clustering. These subtypes showed no significant differences in clinical characteristics but were significantly different in lactate level and patient survivals. Specifically, the subtype demonstrating high interdependence of ADC and rCBV displayed a higher lactate level than the other two subtypes (P = .016 and P = .044, respectively). Both subtypes of low and high interdependence showed worse progression-free survival than the intermediate (P = .046 and P = .009 respectively). Our results suggest that the interdependence between perfusion and diffusion imaging may be useful in stratifying patients and evaluating tumor invasiveness, providing overall measure of tumor microenvironment using multiparametric MRI.
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Affiliation(s)
- Chao Li
- Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK; Department of Neurosurgery, Shanghai General Hospital (originally named Shanghai First People's Hospital), Shanghai Jiao Tong University School of Medicine, China.
| | - Shuo Wang
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Pan Liu
- The Centre for Mathematical Imaging in Healthcare, Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, UK
| | - Turid Torheim
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; CRUK & EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge, UK
| | - Natalie R Boonzaier
- Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK; Developmental Imaging and Biophysics Section, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Bart Rj van Dijken
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Carola-Bibiane Schönlieb
- The Centre for Mathematical Imaging in Healthcare, Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, UK
| | - Florian Markowetz
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; CRUK & EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge, UK
| | - Stephen J Price
- Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK; Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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Calatroni L, d’Autume M, Hocking R, Panayotova S, Parisotto S, Ricciardi P, Schönlieb CB. Unveiling the invisible: mathematical methods for restoring and interpreting illuminated manuscripts. Herit Sci 2018; 6:56. [PMID: 31258910 PMCID: PMC6559148 DOI: 10.1186/s40494-018-0216-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Accepted: 08/20/2018] [Indexed: 06/09/2023]
Abstract
The last 50 years have seen an impressive development of mathematical methods for the analysis and processing of digital images, mostly in the context of photography, biomedical imaging and various forms of engineering. The arts have been mostly overlooked in this process, apart from a few exceptional works in the last 10 years. With the rapid emergence of digitisation in the arts, however, the arts domain is becoming increasingly receptive to digital image processing methods and the importance of paying attention to this therefore increases. In this paper we discuss a range of mathematical methods for digital image restoration and digital visualisation for illuminated manuscripts. The latter provide an interesting opportunity for digital manipulation because they traditionally remain physically untouched. At the same time they also serve as an example for the possibilities mathematics and digital restoration offer as a generic and objective toolkit for the arts.
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Affiliation(s)
- Luca Calatroni
- CMAP, École Polytechnique, Route de Saclay, 91128 Palaiseau, France
| | - Marie d’Autume
- CMLA, ENS Cachan, CNRS, Université Paris-Saclay, 61 Avenue President Wilson, 94235 Cachan, France
| | - Rob Hocking
- DAMTP, University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA UK
| | - Stella Panayotova
- Fitzwilliam Museum, University of Cambridge, Trumpington Street, Cambridge, CB2 1RB UK
| | - Simone Parisotto
- CCA, University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA UK
| | - Paola Ricciardi
- Fitzwilliam Museum, University of Cambridge, Trumpington Street, Cambridge, CB2 1RB UK
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Grah JS, Harrington JA, Koh SB, Pike JA, Schreiner A, Burger M, Schönlieb CB, Reichelt S. Mathematical imaging methods for mitosis analysis in live-cell phase contrast microscopy. Methods 2017; 115:91-99. [PMID: 28189773 PMCID: PMC6414815 DOI: 10.1016/j.ymeth.2017.02.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 02/04/2017] [Accepted: 02/06/2017] [Indexed: 11/25/2022] Open
Abstract
In this paper we propose a workflow to detect and track mitotic cells in time-lapse microscopy image sequences. In order to avoid the requirement for cell lines expressing fluorescent markers and the associated phototoxicity, phase contrast microscopy is often preferred over fluorescence microscopy in live-cell imaging. However, common specific image characteristics complicate image processing and impede use of standard methods. Nevertheless, automated analysis is desirable due to manual analysis being subjective, biased and extremely time-consuming for large data sets. Here, we present the following workflow based on mathematical imaging methods. In the first step, mitosis detection is performed by means of the circular Hough transform. The obtained circular contour subsequently serves as an initialisation for the tracking algorithm based on variational methods. It is sub-divided into two parts: in order to determine the beginning of the whole mitosis cycle, a backwards tracking procedure is performed. After that, the cell is tracked forwards in time until the end of mitosis. As a result, the average of mitosis duration and ratios of different cell fates (cell death, no division, division into two or more daughter cells) can be measured and statistics on cell morphologies can be obtained. All of the tools are featured in the user-friendly MATLAB®Graphical User Interface MitosisAnalyser.
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Affiliation(s)
- Joana Sarah Grah
- University of Cambridge, Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, Wilberforce Road, Cambridge CB3 0WA, United Kingdom.
| | - Jennifer Alison Harrington
- University of Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom
| | - Siang Boon Koh
- University of Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom
| | - Jeremy Andrew Pike
- University of Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom
| | - Alexander Schreiner
- University of Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom
| | - Martin Burger
- Westfälische Wilhelms-Universität Münster, Institute for Computational and Applied Mathematics, Einsteinstrasse 62, 48149 Münster, Germany
| | - Carola-Bibiane Schönlieb
- University of Cambridge, Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, Wilberforce Road, Cambridge CB3 0WA, United Kingdom
| | - Stefanie Reichelt
- University of Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom
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Benning M, Möller M, Nossek RZ, Burger M, Cremers D, Gilboa G, Schönlieb CB. Nonlinear Spectral Image Fusion. Lecture Notes in Computer Science 2017. [DOI: 10.1007/978-3-319-58771-4_4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Burger M, Papafitsoros K, Papoutsellis E, Schönlieb CB. Infimal Convolution Regularisation Functionals of BV and [Formula: see text] Spaces: Part I: The Finite [Formula: see text] Case. J Math Imaging Vis 2016; 55:343-369. [PMID: 27471345 PMCID: PMC4944669 DOI: 10.1007/s10851-015-0624-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2015] [Accepted: 12/09/2015] [Indexed: 06/06/2023]
Abstract
We study a general class of infimal convolution type regularisation functionals suitable for applications in image processing. These functionals incorporate a combination of the total variation seminorm and [Formula: see text] norms. A unified well-posedness analysis is presented and a detailed study of the one-dimensional model is performed, by computing exact solutions for the corresponding denoising problem and the case [Formula: see text]. Furthermore, the dependency of the regularisation properties of this infimal convolution approach to the choice of p is studied. It turns out that in the case [Formula: see text] this regulariser is equivalent to the Huber-type variant of total variation regularisation. We provide numerical examples for image decomposition as well as for image denoising. We show that our model is capable of eliminating the staircasing effect, a well-known disadvantage of total variation regularisation. Moreover as p increases we obtain almost piecewise affine reconstructions, leading also to a better preservation of hat-like structures.
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Affiliation(s)
- Martin Burger
- />Institute for Computational and Applied Mathematics, University of Münster, Münster, Germany
| | | | - Evangelos Papoutsellis
- />Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Carola-Bibiane Schönlieb
- />Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
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Benning M, Gladden L, Holland D, Schönlieb CB, Valkonen T. Phase reconstruction from velocity-encoded MRI measurements--a survey of sparsity-promoting variational approaches. J Magn Reson 2014; 238:26-43. [PMID: 24291331 DOI: 10.1016/j.jmr.2013.10.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2013] [Revised: 10/02/2013] [Accepted: 10/06/2013] [Indexed: 05/12/2023]
Abstract
In recent years there has been significant developments in the reconstruction of magnetic resonance velocity images from sub-sampled k-space data. While showing a strong improvement in reconstruction quality compared to classical approaches, the vast number of different methods, and the challenges in setting them up, often leaves the user with the difficult task of choosing the correct approach, or more importantly, not selecting a poor approach. In this paper, we survey variational approaches for the reconstruction of phase-encoded magnetic resonance velocity images from sub-sampled k-space data. We are particularly interested in regularisers that correctly treat both smooth and geometric features of the image. These features are common to velocity imaging, where the flow field will be smooth but interfaces between the fluid and surrounding material will be sharp, but are challenging to represent sparsely. As an example we demonstrate the variational approaches on velocity imaging of water flowing through a packed bed of solid particles. We evaluate Wavelet regularisation against Total Variation and the relatively recent second order Total Generalised Variation regularisation. We combine these regularisation schemes with a contrast enhancement approach called Bregman iteration. We verify for a variety of sampling patterns that Morozov's discrepancy principle provides a good criterion for stopping the iterations. Therefore, given only the noise level, we present a robust guideline for setting up a variational reconstruction scheme for MR velocity imaging.
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Affiliation(s)
- Martin Benning
- Magnetic Resonance Research Centre, Department of Chemical Engineering and Biotechnology, c/o Cavendish Stores, JJ Thomson Avenue, Cambridge CB3 0HE, United Kingdom.
| | - Lynn Gladden
- Magnetic Resonance Research Centre, Department of Chemical Engineering and Biotechnology, c/o Cavendish Stores, JJ Thomson Avenue, Cambridge CB3 0HE, United Kingdom
| | - Daniel Holland
- Magnetic Resonance Research Centre, Department of Chemical Engineering and Biotechnology, c/o Cavendish Stores, JJ Thomson Avenue, Cambridge CB3 0HE, United Kingdom
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK; Magnetic Resonance Research Centre, Department of Chemical Engineering and Biotechnology, c/o Cavendish Stores, JJ Thomson Avenue, Cambridge CB3 0HE, United Kingdom
| | - Tuomo Valkonen
- Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK; Magnetic Resonance Research Centre, Department of Chemical Engineering and Biotechnology, c/o Cavendish Stores, JJ Thomson Avenue, Cambridge CB3 0HE, United Kingdom
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Carlos De los Reyes J, Schönlieb CB. Image denoising: Learning the noise model via nonsmooth PDE-constrained optimization. ACTA ACUST UNITED AC 2013. [DOI: 10.3934/ipi.2013.7.1183] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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