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Hewlett M, Petrov I, Johnson PM, Drangova M. Deep-learning-based motion correction using multichannel MRI data: a study using simulated artifacts in the fastMRI dataset. NMR IN BIOMEDICINE 2024; 37:e5179. [PMID: 38808752 DOI: 10.1002/nbm.5179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 04/21/2024] [Accepted: 04/29/2024] [Indexed: 05/30/2024]
Abstract
Deep learning presents a generalizable solution for motion correction requiring no pulse sequence modifications or additional hardware, but previous networks have all been applied to coil-combined data. Multichannel MRI data provide a degree of spatial encoding that may be useful for motion correction. We hypothesize that incorporating deep learning for motion correction prior to coil combination will improve results. A conditional generative adversarial network was trained using simulated rigid motion artifacts in brain images acquired at multiple sites with multiple contrasts (not limited to healthy subjects). We compared the performance of deep-learning-based motion correction on individual channel images (single-channel model) with that performed after coil combination (channel-combined model). We also investigate simultaneous motion correction of all channel data from an image volume (multichannel model). The single-channel model significantly (p < 0.0001) improved mean absolute error, with an average 50.9% improvement compared with the uncorrected images. This was significantly (p < 0.0001) better than the 36.3% improvement achieved by the channel-combined model (conventional approach). The multichannel model provided no significant improvement in quantitative measures of image quality compared with the uncorrected images. Results were independent of the presence of pathology, and generalizable to a new center unseen during training. Performing motion correction on single-channel images prior to coil combination provided an improvement in performance compared with conventional deep-learning-based motion correction. Improved deep learning methods for retrospective correction of motion-affected MR images could reduce the need for repeat scans if applied in a clinical setting.
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Affiliation(s)
- Miriam Hewlett
- Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada
| | - Ivailo Petrov
- Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada
| | - Patricia M Johnson
- Department of Radiology, New York Medicine School of Medicine, New York, New York, USA
| | - Maria Drangova
- Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada
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Eguia H, Sánchez-Bocanegra CL, Vinciarelli F, Alvarez-Lopez F, Saigí-Rubió F. Clinical Decision Support and Natural Language Processing in Medicine: Systematic Literature Review. J Med Internet Res 2024; 26:e55315. [PMID: 39348889 DOI: 10.2196/55315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 04/20/2024] [Accepted: 07/24/2024] [Indexed: 10/02/2024] Open
Abstract
BACKGROUND Ensuring access to accurate and verified information is essential for effective patient treatment and diagnosis. Although health workers rely on the internet for clinical data, there is a need for a more streamlined approach. OBJECTIVE This systematic review aims to assess the current state of artificial intelligence (AI) and natural language processing (NLP) techniques in health care to identify their potential use in electronic health records and automated information searches. METHODS A search was conducted in the PubMed, Embase, ScienceDirect, Scopus, and Web of Science online databases for articles published between January 2000 and April 2023. The only inclusion criteria were (1) original research articles and studies on the application of AI-based medical clinical decision support using NLP techniques and (2) publications in English. A Critical Appraisal Skills Programme tool was used to assess the quality of the studies. RESULTS The search yielded 707 articles, from which 26 studies were included (24 original articles and 2 systematic reviews). Of the evaluated articles, 21 (81%) explained the use of NLP as a source of data collection, 18 (69%) used electronic health records as a data source, and a further 8 (31%) were based on clinical data. Only 5 (19%) of the articles showed the use of combined strategies for NLP to obtain clinical data. In total, 16 (62%) articles presented stand-alone data review algorithms. Other studies (n=9, 35%) showed that the clinical decision support system alternative was also a way of displaying the information obtained for immediate clinical use. CONCLUSIONS The use of NLP engines can effectively improve clinical decision systems' accuracy, while biphasic tools combining AI algorithms and human criteria may optimize clinical diagnosis and treatment flows. TRIAL REGISTRATION PROSPERO CRD42022373386; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=373386.
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Affiliation(s)
- Hans Eguia
- SEMERGEN New Technologies Working Group, Madrid, Spain
- Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Barcelona, Spain
| | | | - Franco Vinciarelli
- SEMERGEN New Technologies Working Group, Madrid, Spain
- Emergency Hospital Clemente Álvarez, Rosario (Santa Fe), Argentina
| | | | - Francesc Saigí-Rubió
- Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Barcelona, Spain
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Graf R, Platzek PS, Riedel EO, Kim SH, Lenhart N, Ramschütz C, Paprottka KJ, Kertels OR, Möller HK, Atad M, Bülow R, Werner N, Völzke H, Schmidt CO, Wiestler B, Paetzold JC, Rueckert D, Kirschke JS. Generating synthetic high-resolution spinal STIR and T1w images from T2w FSE and low-resolution axial Dixon. Eur Radiol 2024:10.1007/s00330-024-11047-1. [PMID: 39231829 DOI: 10.1007/s00330-024-11047-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 06/28/2024] [Accepted: 08/19/2024] [Indexed: 09/06/2024]
Abstract
OBJECTIVES To generate sagittal T1-weighted fast spin echo (T1w FSE) and short tau inversion recovery (STIR) images from sagittal T2-weighted (T2w) FSE and axial T1w gradient echo Dixon technique (T1w-Dixon) sequences. MATERIALS AND METHODS This retrospective study used three existing datasets: "Study of Health in Pomerania" (SHIP, 3142 subjects, 1.5 Tesla), "German National Cohort" (NAKO, 2000 subjects, 3 Tesla), and an internal dataset (157 patients 1.5/3 Tesla). We generated synthetic sagittal T1w FSE and STIR images from sagittal T2w FSE and low-resolution axial T1w-Dixon sequences based on two successively applied 3D Pix2Pix deep learning models. "Peak signal-to-noise ratio" (PSNR) and "structural similarity index metric" (SSIM) were used to evaluate the generated image quality on an ablations test. A Turing test, where seven radiologists rated 240 images as either natively acquired or generated, was evaluated using misclassification rate and Fleiss kappa interrater agreement. RESULTS Including axial T1w-Dixon or T1w FSE images resulted in higher image quality in generated T1w FSE (PSNR = 26.942, SSIM = 0.965) and STIR (PSNR = 28.86, SSIM = 0.948) images compared to using only single T2w images as input (PSNR = 23.076/24.677 SSIM = 0.952/0.928). Radiologists had difficulty identifying generated images (misclassification rate: 0.39 ± 0.09 for T1w FSE, 0.42 ± 0.18 for STIR) and showed low interrater agreement on suspicious images (Fleiss kappa: 0.09 for T1w/STIR). CONCLUSIONS Axial T1w-Dixon and sagittal T2w FSE images contain sufficient information to generate sagittal T1w FSE and STIR images. CLINICAL RELEVANCE STATEMENT T1w fast spin echo and short tau inversion recovery can be retroactively added to existing datasets, saving MRI time and enabling retrospective analysis, such as evaluating bone marrow pathologies. KEY POINTS Sagittal T2-weighted images alone were insufficient for differentiating fat and water and to generate T1-weighted images. Axial T1w Dixon technique, together with a T2-weighted sequence, produced realistic sagittal T1-weighted images. Our approach can be used to retrospectively generate STIR and T1-weighted fast spin echo sequences.
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Affiliation(s)
- Robert Graf
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany.
- Institut für KI und Informatik in der Medizin, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
| | - Paul-Sören Platzek
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Evamaria Olga Riedel
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Su Hwan Kim
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Nicolas Lenhart
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Constanze Ramschütz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Karolin Johanna Paprottka
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Olivia Ruriko Kertels
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Hendrik Kristian Möller
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- Institut für KI und Informatik in der Medizin, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Matan Atad
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
- Institut für KI und Informatik in der Medizin, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Robin Bülow
- Institute for Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Nicole Werner
- Institut für Community Medicine, Abteilung SHIP-KEF, University Medicine Greifswald, Greifswald, Germany
| | - Henry Völzke
- Institut für Community Medicine, Abteilung SHIP-KEF, University Medicine Greifswald, Greifswald, Germany
| | - Carsten Oliver Schmidt
- Institut für Community Medicine, Abteilung SHIP-KEF, University Medicine Greifswald, Greifswald, Germany
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Johannes C Paetzold
- Professor of Visual Information Processing, Department of Computing, Imperial College London, London, United Kingdom
| | - Daniel Rueckert
- Institut für KI und Informatik in der Medizin, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Professor of Visual Information Processing, Department of Computing, Imperial College London, London, United Kingdom
| | - Jan Stefan Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
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Liu W, Wu Y, Zheng Z, Yu W, Bittle MJ, Kharrazi H. Evaluating artificial intelligence's role in lung nodule diagnostics: A survey of radiologists in two pilot tertiary hospitals in China. J Clin Imaging Sci 2024; 14:31. [PMID: 39246733 PMCID: PMC11380818 DOI: 10.25259/jcis_72_2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2024] [Accepted: 07/13/2024] [Indexed: 09/10/2024] Open
Abstract
Objectives This study assesses the perceptions and attitudes of Chinese radiologists concerning the application of artificial intelligence (AI) in the diagnosis of lung nodules. Material and Methods An anonymous questionnaire, consisting of 26 questions addressing the usability of AI systems and comprehensive evaluation of AI technology, was distributed to all radiologists affiliated with Beijing Anzhen Hospital and Beijing Tsinghua Changgung Hospital. The data collection was conducted between July 19, and 21, 2023. Results Of the 90 respondents, the majority favored the AI system's convenience and usability, reflected in "good" system usability scale (SUS) scores (Mean ± standard deviation [SD]: 74.3 ± 11.9). General usability was similarly well-received (Mean ± SD: 76.0 ± 11.5), while learnability was rated as "acceptable" (Mean ± SD: 67.5 ± 26.4). Most radiologists noted increased work efficiency (Mean Likert scale score: 4.6 ± 0.6) and diagnostic accuracy (Mean Likert scale score: 4.2 ± 0.8) with the AI system. Views on AI's future impact on radiology careers varied (Mean ± SD: 3.2 ± 1.4), with a consensus that AI is unlikely to replace radiologists entirely in the foreseeable future (Mean ± SD: 2.5 ± 1.1). Conclusion Radiologists at two leading Beijing hospitals generally perceive the AI-assisted lung nodule diagnostic system positively, citing its user-friendliness and effectiveness. However, the system's learnability requires enhancement. While AI is seen as beneficial for work efficiency and diagnostic accuracy, its long-term career implications remain a topic of debate.
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Affiliation(s)
- Weiqi Liu
- Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, United States
| | - You Wu
- Institute for Hospital Management, School of Medicine, Tsinghua University, Beijing, China
| | - Zhuozhao Zheng
- Department of Radiology, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China
| | - Wei Yu
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Mark J Bittle
- Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, United States
| | - Hadi Kharrazi
- Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, United States
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Kamineni M, Raghu V, Truong B, Alaa A, Schuermans A, Friedman S, Reeder C, Bhattacharya R, Libby P, Ellinor PT, Maddah M, Philippakis A, Hornsby W, Yu Z, Natarajan P. Deep learning-derived splenic radiomics, genomics, and coronary artery disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.16.24312129. [PMID: 39185532 PMCID: PMC11343250 DOI: 10.1101/2024.08.16.24312129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Background Despite advances in managing traditional risk factors, coronary artery disease (CAD) remains the leading cause of mortality. Circulating hematopoietic cells influence risk for CAD, but the role of a key regulating organ, spleen, is unknown. The understudied spleen is a 3-dimensional structure of the hematopoietic system optimally suited for unbiased radiologic investigations toward novel mechanistic insights. Methods Deep learning-based image segmentation and radiomics techniques were utilized to extract splenic radiomic features from abdominal MRIs of 42,059 UK Biobank participants. Regression analysis was used to identify splenic radiomics features associated with CAD. Genome-wide association analyses were applied to identify loci associated with these radiomics features. Overlap between loci associated with CAD and the splenic radiomics features was explored to understand the underlying genetic mechanisms of the role of the spleen in CAD. Results We extracted 107 splenic radiomics features from abdominal MRIs, and of these, 10 features were associated with CAD. Genome-wide association analysis of CAD-associated features identified 219 loci, including 35 previously reported CAD loci, 7 of which were not associated with conventional CAD risk factors. Notably, variants at 9p21 were associated with splenic features such as run length non-uniformity. Conclusions Our study, combining deep learning with genomics, presents a new framework to uncover the splenic axis of CAD. Notably, our study provides evidence for the underlying genetic connection between the spleen as a candidate causal tissue-type and CAD with insight into the mechanisms of 9p21, whose mechanism is still elusive despite its initial discovery in 2007. More broadly, our study provides a unique application of deep learning radiomics to non-invasively find associations between imaging, genetics, and clinical outcomes.
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Valošek J, Cohen-Adad J. Reproducible Spinal Cord Quantitative MRI Analysis with the Spinal Cord Toolbox. Magn Reson Med Sci 2024; 23:307-315. [PMID: 38479843 PMCID: PMC11234946 DOI: 10.2463/mrms.rev.2023-0159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/02/2024] Open
Abstract
The spinal cord plays a pivotal role in the central nervous system, providing communication between the brain and the body and containing critical motor and sensory networks. Recent advancements in spinal cord MRI data acquisition and image analysis have shown a potential to improve the diagnostics, prognosis, and management of a variety of pathological conditions. In this review, we first discuss the significance of standardized spinal cord MRI acquisition protocol in multi-center and multi-manufacturer studies. Then, we cover open-access spinal cord MRI datasets, which are important for reproducible science and validation of new methods. Finally, we elaborate on the recent advances in spinal cord MRI data analysis techniques implemented in the open-source software package Spinal Cord Toolbox (SCT).
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Affiliation(s)
- Jan Valošek
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Department of Neurosurgery, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia
- Department of Neurology, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
- Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montreal, QC, Canada
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Rockall AG, Li X, Johnson N, Lavdas I, Santhakumaran S, Prevost AT, Punwani S, Goh V, Barwick TD, Bharwani N, Sandhu A, Sidhu H, Plumb A, Burn J, Fagan A, Wengert GJ, Koh DM, Reczko K, Dou Q, Warwick J, Liu X, Messiou C, Tunariu N, Boavida P, Soneji N, Johnston EW, Kelly-Morland C, De Paepe KN, Sokhi H, Wallitt K, Lakhani A, Russell J, Salib M, Vinnicombe S, Haq A, Aboagye EO, Taylor S, Glocker B. Development and Evaluation of Machine Learning in Whole-Body Magnetic Resonance Imaging for Detecting Metastases in Patients With Lung or Colon Cancer: A Diagnostic Test Accuracy Study. Invest Radiol 2023; 58:823-831. [PMID: 37358356 PMCID: PMC10662596 DOI: 10.1097/rli.0000000000000996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 05/01/2023] [Indexed: 06/27/2023]
Abstract
OBJECTIVES Whole-body magnetic resonance imaging (WB-MRI) has been demonstrated to be efficient and cost-effective for cancer staging. The study aim was to develop a machine learning (ML) algorithm to improve radiologists' sensitivity and specificity for metastasis detection and reduce reading times. MATERIALS AND METHODS A retrospective analysis of 438 prospectively collected WB-MRI scans from multicenter Streamline studies (February 2013-September 2016) was undertaken. Disease sites were manually labeled using Streamline reference standard. Whole-body MRI scans were randomly allocated to training and testing sets. A model for malignant lesion detection was developed based on convolutional neural networks and a 2-stage training strategy. The final algorithm generated lesion probability heat maps. Using a concurrent reader paradigm, 25 radiologists (18 experienced, 7 inexperienced in WB-/MRI) were randomly allocated WB-MRI scans with or without ML support to detect malignant lesions over 2 or 3 reading rounds. Reads were undertaken in the setting of a diagnostic radiology reading room between November 2019 and March 2020. Reading times were recorded by a scribe. Prespecified analysis included sensitivity, specificity, interobserver agreement, and reading time of radiology readers to detect metastases with or without ML support. Reader performance for detection of the primary tumor was also evaluated. RESULTS Four hundred thirty-three evaluable WB-MRI scans were allocated to algorithm training (245) or radiology testing (50 patients with metastases, from primary 117 colon [n = 117] or lung [n = 71] cancer). Among a total 562 reads by experienced radiologists over 2 reading rounds, per-patient specificity was 86.2% (ML) and 87.7% (non-ML) (-1.5% difference; 95% confidence interval [CI], -6.4%, 3.5%; P = 0.39). Sensitivity was 66.0% (ML) and 70.0% (non-ML) (-4.0% difference; 95% CI, -13.5%, 5.5%; P = 0.344). Among 161 reads by inexperienced readers, per-patient specificity in both groups was 76.3% (0% difference; 95% CI, -15.0%, 15.0%; P = 0.613), with sensitivity of 73.3% (ML) and 60.0% (non-ML) (13.3% difference; 95% CI, -7.9%, 34.5%; P = 0.313). Per-site specificity was high (>90%) for all metastatic sites and experience levels. There was high sensitivity for the detection of primary tumors (lung cancer detection rate of 98.6% with and without ML [0.0% difference; 95% CI, -2.0%, 2.0%; P = 1.00], colon cancer detection rate of 89.0% with and 90.6% without ML [-1.7% difference; 95% CI, -5.6%, 2.2%; P = 0.65]). When combining all reads from rounds 1 and 2, reading times fell by 6.2% (95% CI, -22.8%, 10.0%) when using ML. Round 2 read-times fell by 32% (95% CI, 20.8%, 42.8%) compared with round 1. Within round 2, there was a significant decrease in read-time when using ML support, estimated as 286 seconds (or 11%) quicker ( P = 0.0281), using regression analysis to account for reader experience, read round, and tumor type. Interobserver variance suggests moderate agreement, Cohen κ = 0.64; 95% CI, 0.47, 0.81 (with ML), and Cohen κ = 0.66; 95% CI, 0.47, 0.81 (without ML). CONCLUSIONS There was no evidence of a significant difference in per-patient sensitivity and specificity for detecting metastases or the primary tumor using concurrent ML compared with standard WB-MRI. Radiology read-times with or without ML support fell for round 2 reads compared with round 1, suggesting that readers familiarized themselves with the study reading method. During the second reading round, there was a significant reduction in reading time when using ML support.
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Sjöholm T, Tarai S, Malmberg F, Strand R, Korenyushkin A, Enblad G, Ahlström H, Kullberg J. A whole-body diffusion MRI normal atlas: development, evaluation and initial use. Cancer Imaging 2023; 23:87. [PMID: 37710346 PMCID: PMC10503210 DOI: 10.1186/s40644-023-00603-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 08/28/2023] [Indexed: 09/16/2023] Open
Abstract
BACKGROUND Statistical atlases can provide population-based descriptions of healthy volunteers and/or patients and can be used for region- and voxel-based analysis. This work aims to develop whole-body diffusion atlases of healthy volunteers scanned at 1.5T and 3T. Further aims include evaluating the atlases by establishing whole-body Apparent Diffusion Coefficient (ADC) values of healthy tissues and including healthy tissue deviations in an automated tumour segmentation task. METHODS Multi-station whole-body Diffusion Weighted Imaging (DWI) and water-fat Magnetic Resonance Imaging (MRI) of healthy volunteers (n = 45) were acquired at 1.5T (n = 38) and/or 3T (n = 29), with test-retest imaging for five subjects per scanner. Using deformable image registration, whole-body MRI data was registered and composed into normal atlases. Healthy tissue ADCmean was manually measured for ten tissues, with test-retest percentage Repeatability Coefficient (%RC), and effect of age, sex and scanner assessed. Voxel-wise whole-body analyses using the normal atlases were studied with ADC correlation analyses and an automated tumour segmentation task. For the latter, lymphoma patient MRI scans (n = 40) with and without information about healthy tissue deviations were entered into a 3D U-Net architecture. RESULTS Sex- and Body Mass Index (BMI)-stratified whole-body high b-value DWI and ADC normal atlases were created at 1.5T and 3T. %RC of healthy tissue ADCmean varied depending on tissue assessed (4-48% at 1.5T, 6-70% at 3T). Scanner differences in ADCmean were visualised in Bland-Altman analyses of dually scanned subjects. Sex differences were measurable for liver, muscle and bone at 1.5T, and muscle at 3T. Volume of Interest (VOI)-based multiple linear regression, and voxel-based correlations in normal atlas space, showed that age and ADC were negatively associated for liver and bone at 1.5T, and positively associated with brain tissue at 1.5T and 3T. Adding voxel-wise information about healthy tissue deviations in an automated tumour segmentation task gave numerical improvements in the segmentation metrics Dice score, sensitivity and precision. CONCLUSIONS Whole-body DWI and ADC normal atlases were created at 1.5T and 3T, and applied in whole-body voxel-wise analyses.
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Affiliation(s)
- Therese Sjöholm
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Sambit Tarai
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Filip Malmberg
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Robin Strand
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | | | - Gunilla Enblad
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Håkan Ahlström
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Antaros Medical AB, Mölndal, Sweden
| | - Joel Kullberg
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
- Antaros Medical AB, Mölndal, Sweden.
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Automated imaging-based abdominal organ segmentation and quality control in 20,000 participants of the UK Biobank and German National Cohort Studies. Sci Rep 2022; 12:18733. [PMID: 36333523 PMCID: PMC9636393 DOI: 10.1038/s41598-022-23632-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 11/02/2022] [Indexed: 11/06/2022] Open
Abstract
Large epidemiological studies such as the UK Biobank (UKBB) or German National Cohort (NAKO) provide unprecedented health-related data of the general population aiming to better understand determinants of health and disease. As part of these studies, Magnetic Resonance Imaging (MRI) is performed in a subset of participants allowing for phenotypical and functional characterization of different organ systems. Due to the large amount of imaging data, automated image analysis is required, which can be performed using deep learning methods, e. g. for automated organ segmentation. In this paper we describe a computational pipeline for automated segmentation of abdominal organs on MRI data from 20,000 participants of UKBB and NAKO and provide results of the quality control process. We found that approx. 90% of data sets showed no relevant segmentation errors while relevant errors occurred in a varying proportion of data sets depending on the organ of interest. Image-derived features based on automated organ segmentations showed relevant deviations of varying degree in the presence of segmentation errors. These results show that large-scale, deep learning-based abdominal organ segmentation on MRI data is feasible with overall high accuracy, but visual quality control remains an important step ensuring the validity of down-stream analyses in large epidemiological imaging studies.
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10
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Satchwell L, Wedlake L, Greenlay E, Li X, Messiou C, Glocker B, Barwick T, Barfoot T, Doran S, Leach MO, Koh DM, Kaiser M, Winzeck S, Qaiser T, Aboagye E, Rockall A. Development of machine learning support for reading whole body diffusion-weighted MRI (WB-MRI) in myeloma for the detection and quantification of the extent of disease before and after treatment (MALIMAR): protocol for a cross-sectional diagnostic test accuracy study. BMJ Open 2022; 12:e067140. [PMID: 36198471 PMCID: PMC9535185 DOI: 10.1136/bmjopen-2022-067140] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
INTRODUCTION Whole-body MRI (WB-MRI) is recommended by the National Institute of Clinical Excellence as the first-line imaging tool for diagnosis of multiple myeloma. Reporting WB-MRI scans requires expertise to interpret and can be challenging for radiologists who need to meet rapid turn-around requirements. Automated computational tools based on machine learning (ML) could assist the radiologist in terms of sensitivity and reading speed and would facilitate improved accuracy, productivity and cost-effectiveness. The MALIMAR study aims to develop and validate a ML algorithm to increase the diagnostic accuracy and reading speed of radiological interpretation of WB-MRI compared with standard methods. METHODS AND ANALYSIS This phase II/III imaging trial will perform retrospective analysis of previously obtained clinical radiology MRI scans and scans from healthy volunteers obtained prospectively to implement training and validation of an ML algorithm. The study will comprise three project phases using approximately 633 scans to (1) train the ML algorithm to identify active disease, (2) clinically validate the ML algorithm and (3) determine change in disease status following treatment via a quantification of burden of disease in patients with myeloma. Phase 1 will primarily train the ML algorithm to detect active myeloma against an expert assessment ('reference standard'). Phase 2 will use the ML output in the setting of radiology reader study to assess the difference in sensitivity when using ML-assisted reading or human-alone reading. Phase 3 will assess the agreement between experienced readers (with and without ML) and the reference standard in scoring both overall burden of disease before and after treatment, and response. ETHICS AND DISSEMINATION MALIMAR has ethical approval from South Central-Oxford C Research Ethics Committee (REC Reference: 17/SC/0630). IRAS Project ID: 233501. CPMS Portfolio adoption (CPMS ID: 36766). Participants gave informed consent to participate in the study before taking part. MALIMAR is funded by National Institute for Healthcare Research Efficacy and Mechanism Evaluation funding (NIHR EME Project ID: 16/68/34). Findings will be made available through peer-reviewed publications and conference dissemination. TRIAL REGISTRATION NUMBER NCT03574454.
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Affiliation(s)
| | | | | | - Xingfeng Li
- Department of Cancer and Surgery, Imperial College London, London, UK
| | - Christina Messiou
- Royal Marsden Hospital NHS Trust, London, UK
- Institute of Cancer Research, London, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Tara Barwick
- Department of Cancer and Surgery, Imperial College London, London, UK
- Department of Radiology, Imperial College Healthcare NHS Trust, London, UK
| | | | | | | | - Dow Mu Koh
- Royal Marsden Hospital NHS Trust, London, UK
- Institute of Cancer Research, London, UK
| | - Martin Kaiser
- Royal Marsden Hospital NHS Trust, London, UK
- Institute of Cancer Research, London, UK
| | - Stefan Winzeck
- Department of Computing, Imperial College London, London, UK
| | - Talha Qaiser
- Department of Computing, Imperial College London, London, UK
| | - Eric Aboagye
- Department of Cancer and Surgery, Imperial College London, London, UK
| | - Andrea Rockall
- Department of Cancer and Surgery, Imperial College London, London, UK
- Department of Radiology, Imperial College Healthcare NHS Trust, London, UK
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11
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Rata M, Blackledge M, Scurr E, Winfield J, Koh DM, Dragan A, Candito A, King A, Rennie W, Gaba S, Suresh P, Malcolm P, Davis A, Nilak A, Shah A, Gandhi S, Albrizio M, Drury A, Roberts S, Jenner M, Brown S, Kaiser M, Messiou C. Implementation of Whole-Body MRI (MY-RADS) within the OPTIMUM/MUKnine multi-centre clinical trial for patients with myeloma. Insights Imaging 2022; 13:123. [PMID: 35900614 PMCID: PMC9334517 DOI: 10.1186/s13244-022-01253-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 06/14/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Whole-body (WB) MRI, which includes diffusion-weighted imaging (DWI) and T1-w Dixon, permits sensitive detection of marrow disease in addition to qualitative and quantitative measurements of disease and response to treatment of bone marrow. We report on the first study to embed standardised WB-MRI within a prospective, multi-centre myeloma clinical trial (IMAGIMM trial, sub-study of OPTIMUM/MUKnine) to explore the use of WB-MRI to detect minimal residual disease after treatment. METHODS The standardised MY-RADS WB-MRI protocol was set up on a local 1.5 T scanner. An imaging manual describing the MR protocol, quality assurance/control procedures and data transfer was produced and provided to sites. For non-identical scanners (different vendor or magnet strength), site visits from our physics team were organised to support protocol optimisation. The site qualification process included review of phantom and volunteer data acquired at each site and a teleconference to brief the multidisciplinary team. Image quality of initial patients at each site was assessed. RESULTS WB-MRI was successfully set up at 12 UK sites involving 3 vendor systems and two field strengths. Four main protocols (1.5 T Siemens, 3 T Siemens, 1.5 T Philips and 3 T GE scanners) were generated. Scanner limitations (hardware and software) and scanning time constraint required protocol modifications for 4 sites. Nevertheless, shared methodology and imaging protocols enabled other centres to obtain images suitable for qualitative and quantitative analysis. CONCLUSIONS Standardised WB-MRI protocols can be implemented and supported in prospective multi-centre clinical trials. Trial registration NCT03188172 clinicaltrials.gov; registration date 15th June 2017 https://clinicaltrials.gov/ct2/show/study/NCT03188172.
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Affiliation(s)
- Mihaela Rata
- Royal Marsden NHS Foundation Trust and Institute of Cancer Research, Downs Road, SM2 5PT, Sutton, London, UK.
| | - Matthew Blackledge
- Royal Marsden NHS Foundation Trust and Institute of Cancer Research, Downs Road, SM2 5PT, Sutton, London, UK
| | - Erica Scurr
- Royal Marsden NHS Foundation Trust and Institute of Cancer Research, Downs Road, SM2 5PT, Sutton, London, UK
| | - Jessica Winfield
- Royal Marsden NHS Foundation Trust and Institute of Cancer Research, Downs Road, SM2 5PT, Sutton, London, UK
| | - Dow-Mu Koh
- Royal Marsden NHS Foundation Trust and Institute of Cancer Research, Downs Road, SM2 5PT, Sutton, London, UK
| | - Alina Dragan
- Royal Marsden NHS Foundation Trust and Institute of Cancer Research, Downs Road, SM2 5PT, Sutton, London, UK
| | - Antonio Candito
- Royal Marsden NHS Foundation Trust and Institute of Cancer Research, Downs Road, SM2 5PT, Sutton, London, UK
| | - Alexander King
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | | | - Suchi Gaba
- Royal Stoke University Hospital, Stoke-on-Trent, UK
| | - Priya Suresh
- University Hospitals Plymouth NHS Foundation Trust, Plymouth, UK
| | - Paul Malcolm
- Norfolk and Norwich University Hospital, Norwich, UK
| | - Amy Davis
- Epsom and St. Helier University Hospitals NHS Trust, Epsom, UK
| | | | - Aarti Shah
- Basingstoke and North Hampshire Hospital, Basingstoke, UK
| | | | - Mauro Albrizio
- Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Arnold Drury
- Royal Bournemouth and Christchurch Hospitals NHS Foundation Trust, Bournemouth, UK
| | - Sadie Roberts
- University of Leeds Clinical Trial Research Unit, Leeds, UK
| | - Matthew Jenner
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Sarah Brown
- University of Leeds Clinical Trial Research Unit, Leeds, UK
| | - Martin Kaiser
- Royal Marsden NHS Foundation Trust and Institute of Cancer Research, Downs Road, SM2 5PT, Sutton, London, UK
| | - Christina Messiou
- Royal Marsden NHS Foundation Trust and Institute of Cancer Research, Downs Road, SM2 5PT, Sutton, London, UK
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12
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Moore MM, Iyer RS, Sarwani NI, Sze RW. Artificial intelligence development in pediatric body magnetic resonance imaging: best ideas to adapt from adults. Pediatr Radiol 2022; 52:367-373. [PMID: 33851261 PMCID: PMC8043435 DOI: 10.1007/s00247-021-05072-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 02/09/2021] [Accepted: 03/22/2021] [Indexed: 12/22/2022]
Abstract
Emerging manifestations of artificial intelligence (AI) have featured prominently in virtually all industries and facets of our lives. Within the radiology literature, AI has shown great promise in improving and augmenting radiologist workflow. In pediatric imaging, while greatest AI inroads have been made in musculoskeletal radiographs, there are certainly opportunities within thoracoabdominal MRI for AI to add significant value. In this paper, we briefly review non-interpretive and interpretive data science, with emphasis on potential avenues for advancement in pediatric body MRI based on similar work in adults. The discussion focuses on MRI image optimization, abdominal organ segmentation, and osseous lesion detection encountered during body MRI in children.
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Affiliation(s)
- Michael M Moore
- Department of Radiology, Penn State Children's Hospital, Penn State Health, 500 University Drive, H066, Hershey, PA, 17033, USA.
| | - Ramesh S Iyer
- Seattle Children's Hospital, University of Washington, Seattle, WA, USA
| | | | - Raymond W Sze
- Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
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13
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Song H, Bak S, Kim I, Woo JY, Cho EJ, Choi YJ, Rha SE, Oh SA, Youn SY, Lee SJ. An Application of Machine Learning That Uses the Magnetic Resonance Imaging Metric, Mean Apparent Diffusion Coefficient, to Differentiate between the Histological Types of Ovarian Cancer. J Clin Med 2021; 11:jcm11010229. [PMID: 35011970 PMCID: PMC8745699 DOI: 10.3390/jcm11010229] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 12/21/2021] [Accepted: 12/29/2021] [Indexed: 12/13/2022] Open
Abstract
This retrospective single-center study included patients diagnosed with epithelial ovarian cancer (EOC) using preoperative pelvic magnetic resonance imaging (MRI). The apparent diffusion coefficient (ADC) of the axial MRI maps that included the largest solid portion of the ovarian mass was analysed. The mean ADC values (ADCmean) were derived from the regions of interest (ROIs) of each largest solid portion. Logistic regression and three types of machine learning (ML) applications were used to analyse the ADCs and clinical factors. Of the 200 patients, 103 had high-grade serous ovarian cancer (HGSOC), and 97 had non-HGSOC (endometrioid carcinoma, clear cell carcinoma, mucinous carcinoma, and low-grade serous ovarian cancer). The median ADCmean of patients with HGSOC was significantly lower than that of patients without HGSOCs. Low ADCmean and CA 19-9 levels were independent predictors for HGSOC over non-HGSOC. Compared to stage I disease, stage III disease was associated with HGSOC. Gradient boosting machine and extreme gradient boosting machine showed the highest accuracy in distinguishing between the histological findings of HGSOC versus non-HGSOC and between the five histological types of EOC. In conclusion, ADCmean, disease stage at diagnosis, and CA 19-9 level were significant factors for differentiating between EOC histological types.
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Affiliation(s)
- Heekyoung Song
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea; (H.S.); (S.B.); (I.K.); (J.Y.W.); (E.J.C.); (Y.J.C.)
| | - Seongeun Bak
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea; (H.S.); (S.B.); (I.K.); (J.Y.W.); (E.J.C.); (Y.J.C.)
| | - Imhyeon Kim
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea; (H.S.); (S.B.); (I.K.); (J.Y.W.); (E.J.C.); (Y.J.C.)
| | - Jae Yeon Woo
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea; (H.S.); (S.B.); (I.K.); (J.Y.W.); (E.J.C.); (Y.J.C.)
| | - Eui Jin Cho
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea; (H.S.); (S.B.); (I.K.); (J.Y.W.); (E.J.C.); (Y.J.C.)
| | - Youn Jin Choi
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea; (H.S.); (S.B.); (I.K.); (J.Y.W.); (E.J.C.); (Y.J.C.)
| | - Sung Eun Rha
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea;
| | - Shin Ah Oh
- NAVER Clova, 246, Hwangsaeul-ro, Bundang-gu, Seongnam-si 13595, Korea;
| | - Seo Yeon Youn
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea;
- Correspondence: (S.Y.Y.); (S.J.L.)
| | - Sung Jong Lee
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea; (H.S.); (S.B.); (I.K.); (J.Y.W.); (E.J.C.); (Y.J.C.)
- Correspondence: (S.Y.Y.); (S.J.L.)
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14
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Ferjaoui R, Cherni MA, Boujnah S, Kraiem NEH, Kraiem T. Machine learning for evolutive lymphoma and residual masses recognition in whole body diffusion weighted magnetic resonance images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 209:106320. [PMID: 34390938 DOI: 10.1016/j.cmpb.2021.106320] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 07/25/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND After the treatment of the patients with malignant lymphoma, there may persist lesions that must be labeled either as evolutive lymphoma requiring new treatments or as residual masses. We present in this work, a machine learning-based computer-aided diagnosis (CAD) applied to whole-body diffusion-weighted magnetic resonance images. METHODS The database consists of a total of 1005 MRI images with evolutive lymphoma and residual masses. More specifically, we propose a novel approach that leverages: (1)-The complementarity of the functional and anatomical criteria of MRI images through a fusion step based on the discrete wavelet transforms (DWT). (2)- The automatic segmentation of the lesions, their localization, and their enumeration using the Chan-Vese algorithm. (3)- The generation of the parametric image which contains the apparent diffusion coefficient value named ADC map. (4)- The features selection through the application of the sequential forward selection (SFS), Entropy, Symmetric uncertainty and Gain Ratio algorithm on 72 extracted features. (5)- The classification of the lesions by applying five well known supervised machine learning classification algorithms: the back-propagation artificial neural network (ANN), the support vector machine (SVM), the K-nearest neighbours (K-NN), Relevance Vectors Machine (RVM), and the random forest (RF) compared to deep learning based on convolutional neural network (CNN). Moreover, this study is achieved with an evaluation of the classification using 335 DW-MR images where 80% of them are used for the training and the remaining 20% for the test. RESULTS The obtained accuracy for the five classifiers recorded a slight superiority to the proposed method based on the back-propagation 3-9-1 ANN model which reaches 96,5%. In addition, we compared the proposed method to five other works from the literature. The proposed method gives much better results in terms of SE, SP, accuracy, F1-measure, and geometric-mean which reaches respectively 96.4%, 90.9%, 95.5%, 0.97, and 91.61%. CONCLUSIONS Our initial results suggest that Combining functional, anatomical, and morphological features of ROI's have very good accuracy (97.01%) for evolutive lymphoma and residual masses recognition when we based on the new proposed approach using the back-propagation 3-9-1 ANN model. Proposed method based on machine learning gives less than Deep learning CNN, which is 98.5%.
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Affiliation(s)
- Radhia Ferjaoui
- University of Tunis El Manar, Research Laboratory of biophysics and Medical technologies (LRBTM), ISTMT, Tunis, 1006, Tunisia.
| | - Mohamed Ali Cherni
- University of Tunis, LR13 ES03 SIME Laboratory, ENSIT, Montfleury 1008 Tunisia
| | - Sana Boujnah
- University of Tunis El Manar, National Engineering School of Tunis, Tunisia
| | | | - Tarek Kraiem
- University of Tunis El Manar, Faculty of Medicine of Tunis, Tunis, 1007, Tunisia; University of Tunis El Manar, Research Laboratory of biophysics and Medical technologies (LRBTM), ISTMT, Tunis, 1006, Tunisia
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15
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Kart T, Fischer M, Küstner T, Hepp T, Bamberg F, Winzeck S, Glocker B, Rueckert D, Gatidis S. Deep Learning-Based Automated Abdominal Organ Segmentation in the UK Biobank and German National Cohort Magnetic Resonance Imaging Studies. Invest Radiol 2021; 56:401-408. [PMID: 33930003 DOI: 10.1097/rli.0000000000000755] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
PURPOSE The aims of this study were to train and evaluate deep learning models for automated segmentation of abdominal organs in whole-body magnetic resonance (MR) images from the UK Biobank (UKBB) and German National Cohort (GNC) MR imaging studies and to make these models available to the scientific community for analysis of these data sets. METHODS A total of 200 T1-weighted MR image data sets of healthy volunteers each from UKBB and GNC (400 data sets in total) were available in this study. Liver, spleen, left and right kidney, and pancreas were segmented manually on all 400 data sets, providing labeled ground truth data for training of a previously described U-Net-based deep learning framework for automated medical image segmentation (nnU-Net). The trained models were tested on all data sets using a 4-fold cross-validation scheme. Qualitative analysis of automated segmentation results was performed visually; performance metrics between automated and manual segmentation results were computed for quantitative analysis. In addition, interobserver segmentation variability between 2 human readers was assessed on a subset of the data. RESULTS Automated abdominal organ segmentation was performed with high qualitative and quantitative accuracy on UKBB and GNC data. In more than 90% of data sets, no or only minor visually detectable qualitative segmentation errors occurred. Mean Dice scores of automated segmentations compared with manual reference segmentations were well higher than 0.9 for the liver, spleen, and kidneys on UKBB and GNC data and around 0.82 and 0.89 for the pancreas on UKBB and GNC data, respectively. Mean average symmetric surface distance was between 0.3 and 1.5 mm for the liver, spleen, and kidneys and between 2 and 2.2 mm for pancreas segmentation. The quantitative accuracy of automated segmentation was comparable with the agreement between 2 human readers for all organs on UKBB and GNC data. CONCLUSION Automated segmentation of abdominal organs is possible with high qualitative and quantitative accuracy on whole-body MR imaging data acquired as part of UKBB and GNC. The results obtained and deep learning models trained in this study can be used as a foundation for automated analysis of thousands of MR data sets of UKBB and GNC and thus contribute to tackling topical and original scientific questions.
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Affiliation(s)
- Turkay Kart
- From the Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - Marc Fischer
- Medical Image and Data Analysis Lab, Department of Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Thomas Küstner
- Medical Image and Data Analysis Lab, Department of Radiology, University Hospital Tübingen, Tübingen, Germany
| | | | - Fabian Bamberg
- Department of Radiology, University Hospital Freiburg, Freiburg, Germany
| | - Stefan Winzeck
- From the Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - Ben Glocker
- From the Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
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16
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Van Nieuwenhove S, Van Damme J, Padhani AR, Vandecaveye V, Tombal B, Wuts J, Pasoglou V, Lecouvet FE. Whole-body magnetic resonance imaging for prostate cancer assessment: Current status and future directions. J Magn Reson Imaging 2020; 55:653-680. [PMID: 33382151 DOI: 10.1002/jmri.27485] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 12/08/2020] [Accepted: 12/08/2020] [Indexed: 12/20/2022] Open
Abstract
Over the past decade, updated definitions for the different stages of prostate cancer and risk for distant disease, along with the advent of new therapies, have remarkably changed the management of patients. The two expectations from imaging are accurate staging and appropriate assessment of disease response to therapies. Modern, next-generation imaging (NGI) modalities, including whole-body magnetic resonance imaging (WB-MRI) and nuclear medicine (most often prostate-specific membrane antigen [PSMA] positron emission tomography [PET]/computed tomography [CT]) bring added value to these imaging tasks. WB-MRI has proven its superiority over bone scintigraphy (BS) and CT for the detection of distant metastasis, also providing reliable evaluations of disease response to treatment. Comparison of the effectiveness of WB-MRI and molecular nuclear imaging techniques with regard to indications and the definition of their respective/complementary roles in clinical practice is ongoing. This paper illustrates the evolution of WB-MRI imaging protocols, defines the current state-of-the art, and highlights the latest developments and future challenges. The paper presents and discusses WB-MRI indications in the care pathway of men with prostate cancer in specific key situations: response assessment of metastatic disease, "all in one" cancer staging, and oligometastatic disease.
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Affiliation(s)
- Sandy Van Nieuwenhove
- Department of Radiology and Medical Imaging, Cliniques Universitaires Saint-Luc, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Brussels, Belgium
| | - Julien Van Damme
- Department of Urology, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Anwar R Padhani
- Mount Vernon Cancer Centre, Mount Vernon Hospital, London, UK
| | - Vincent Vandecaveye
- Department of Radiology and Pathology, University Hospitals Leuven, Leuven, Belgium
| | - Bertrand Tombal
- Department of Urology, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Joris Wuts
- Department of Radiology and Medical Imaging, Cliniques Universitaires Saint-Luc, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Brussels, Belgium.,Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Brussels, Belgium
| | - Vassiliki Pasoglou
- Department of Radiology and Medical Imaging, Cliniques Universitaires Saint-Luc, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Brussels, Belgium
| | - Frederic E Lecouvet
- Department of Radiology and Medical Imaging, Cliniques Universitaires Saint-Luc, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Brussels, Belgium
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Osborne B, Bakula D, Ben Ezra M, Dresen C, Hartmann E, Kristensen SM, Mkrtchyan GV, Nielsen MH, Petr MA, Scheibye-Knudsen M. New methodologies in ageing research. Ageing Res Rev 2020; 62:101094. [PMID: 32512174 DOI: 10.1016/j.arr.2020.101094] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Revised: 05/14/2020] [Accepted: 05/27/2020] [Indexed: 02/06/2023]
Abstract
Ageing is arguably the most complex phenotype that occurs in humans. To understand and treat ageing as well as associated diseases, highly specialised technologies are emerging that reveal critical insight into the underlying mechanisms and provide new hope for previously untreated diseases. Herein, we describe the latest developments in cutting edge technologies applied across the field of ageing research. We cover emerging model organisms, high-throughput methodologies and machine-driven approaches. In all, this review will give you a glimpse of what will be pushing the field onwards and upwards.
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Affiliation(s)
- Brenna Osborne
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Daniela Bakula
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Michael Ben Ezra
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Charlotte Dresen
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Esben Hartmann
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Stella M Kristensen
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Garik V Mkrtchyan
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Malte H Nielsen
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Michael A Petr
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Morten Scheibye-Knudsen
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark.
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18
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From hype to hope to hard work: developing responsible AI for radiology. Clin Radiol 2020; 75:1-2. [DOI: 10.1016/j.crad.2019.09.123] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 08/28/2019] [Indexed: 11/20/2022]
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