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Amini A, Shayganfar A, Amini Z, Ostovar L, HajiAhmadi S, Chitsaz N, Rabbani M, Kafieh R. Deep learning for discrimination of active and inactive lesions in multiple sclerosis using non-contrast FLAIR MRI: A multicenter study. Mult Scler Relat Disord 2024; 87:105642. [PMID: 38703520 DOI: 10.1016/j.msard.2024.105642] [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: 11/10/2023] [Revised: 02/18/2024] [Accepted: 04/20/2024] [Indexed: 05/06/2024]
Abstract
BACKGROUND Within the domain of multiple sclerosis (MS), the precise discrimination between active and inactive lesions bears immense significance. Active lesions are enhanced on T1-weighted MRI images after administration of gadolinium-based contrast agents, which brings about associated complexities. This study investigates the potential of deep learning to differentiate between active and inactive lesions in MS using non-contrast FLAIR-type MRI data, presenting a non-invasive alternative to conventional gadolinium-based MRI methods. METHODS The dataset encompasses 9097 lesion images collected from 130 MS patients across four distinct imaging centers, with post-contrast T1-weighted images as the benchmark reference. We initially identified and labeled the lesions and carefully selected corresponding regions of interest (ROIs). These ROIs were employed as inputs for a convolutional neural network (CNN) to predict lesion status. Also, transfer learning was utilized, incorporating 12 pre-trained CNN models. Subsequently, an ensemble technique was applied to 3 of best models, followed by a systematic comparison of the results. RESULTS Through a 5-fold cross-validation, our custom designed network exhibited an average accuracy of 85 %, a sensitivity of 95 %, a specificity of 75 %, and an AUC value of 0.90. Among the pre-trained models, ResNet50 emerged as the most effective, achieving a specificity of 58 %, an accuracy of 75 %, a sensitivity of 91 %, and an AUC value of 0.81. Our comprehensive evaluations encompassed the receiver operating characteristic curve, precision-recall curve, and confusion matrix analyses. CONCLUSION The findings underscore the efficacy of the proposed CNN, trained on FLAIR MRI data ROIs, in accurately discerning active and inactive lesions without reliance on contrast agents. Our multicenter study of 130 patients with diverse imaging devices outperforms the other single-center studies, achieving superior sensitivity and specificity. Unlike studies using multiple modalities, our exclusive use of FLAIR images streamlines the process, and our streamlined approach, excluding conventional pre-processing, demonstrates efficiency. The external validation conducted on diverse datasets, coupled with the analysis of dilated masks, underscores the adaptability and efficacy of our custom CNN model in discerning between active and inactive lesions.
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Affiliation(s)
- AmirAbbas Amini
- School of Advanced Technologies in Medicine, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Azin Shayganfar
- Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Zahra Amini
- School of Advanced Technologies in Medicine, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Leila Ostovar
- Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Somayeh HajiAhmadi
- Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Navid Chitsaz
- Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Masoud Rabbani
- Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Raheleh Kafieh
- Department of Engineering, Durham University, Durham, UK.
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Collorone S, Coll L, Lorenzi M, Lladó X, Sastre-Garriga J, Tintoré M, Montalban X, Rovira À, Pareto D, Tur C. Artificial intelligence applied to MRI data to tackle key challenges in multiple sclerosis. Mult Scler 2024; 30:767-784. [PMID: 38738527 DOI: 10.1177/13524585241249422] [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: 05/14/2024]
Abstract
Artificial intelligence (AI) is the branch of science aiming at creating algorithms able to carry out tasks that typically require human intelligence. In medicine, there has been a tremendous increase in AI applications thanks to increasingly powerful computers and the emergence of big data repositories. Multiple sclerosis (MS) is a chronic autoimmune condition affecting the central nervous system with a complex pathogenesis, a challenging diagnostic process strongly relying on magnetic resonance imaging (MRI) and a high and largely unexplained variability across patients. Therefore, AI applications in MS have the great potential of helping us better support the diagnosis, find markers for prognosis to eventually design more powerful randomised clinical trials and improve patient management in clinical practice and eventually understand the mechanisms of the disease. This topical review aims to summarise the recent advances in AI applied to MRI data in MS to illustrate its achievements, limitations and future directions.
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Affiliation(s)
- Sara Collorone
- NMR Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Llucia Coll
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of Neurology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Marco Lorenzi
- Epione Research Project, Inria Sophia Antipolis, Université Côte d'Azur, Nice, France
| | - Xavier Lladó
- Research Institute of Computer Vision and Robotics, University of Girona, Girona, Spain
| | - Jaume Sastre-Garriga
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of Neurology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Mar Tintoré
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of Neurology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Xavier Montalban
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of Neurology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Àlex Rovira
- Section of Neuroradiology, Department of Radiology (IDI), Vall d'Hebron University Hospital, Barcelona, Spain
- Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology (IDI), Vall d'Hebron University Hospital, Barcelona, Spain
- Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Carmen Tur
- NMR Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of Neurology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
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Ananthavarathan P, Sahi N, Chard DT. An update on the role of magnetic resonance imaging in predicting and monitoring multiple sclerosis progression. Expert Rev Neurother 2024; 24:201-216. [PMID: 38235594 DOI: 10.1080/14737175.2024.2304116] [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: 11/01/2023] [Accepted: 01/08/2024] [Indexed: 01/19/2024]
Abstract
INTRODUCTION While magnetic resonance imaging (MRI) is established in diagnosing and monitoring disease activity in multiple sclerosis (MS), its utility in predicting and monitoring disease progression is less clear. AREAS COVERED The authors consider changing concepts in the phenotypic classification of MS, including progression independent of relapses; pathological processes underpinning progression; advances in MRI measures to assess them; how well MRI features explain and predict clinical outcomes, including models that assess disease effects on neural networks, and the potential role for machine learning. EXPERT OPINION Relapsing-remitting and progressive MS have evolved from being viewed as mutually exclusive to having considerable overlap. Progression is likely the consequence of several pathological elements, each important in building more holistic prognostic models beyond conventional phenotypes. MRI is well placed to assess pathogenic processes underpinning progression, but we need to bridge the gap between MRI measures and clinical outcomes. Mapping pathological effects on specific neural networks may help and machine learning methods may be able to optimize predictive markers while identifying new, or previously overlooked, clinically relevant features. The ever-increasing ability to measure features on MRI raises the dilemma of what to measure and when, and the challenge of translating research methods into clinically useable tools.
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Affiliation(s)
- Piriyankan Ananthavarathan
- Department of Neuroinflammation, University College London Queen Square Multiple Sclerosis Centre, London, UK
| | - Nitin Sahi
- Department of Neuroinflammation, University College London Queen Square Multiple Sclerosis Centre, London, UK
| | - Declan T Chard
- Clinical Research Associate & Consultant Neurologist, Institute of Neurology - Queen Square Multiple Sclerosis Centre, London, UK
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Ekmekyapar T, Taşcı B. Exemplar MobileNetV2-Based Artificial Intelligence for Robust and Accurate Diagnosis of Multiple Sclerosis. Diagnostics (Basel) 2023; 13:3030. [PMID: 37835771 PMCID: PMC10572467 DOI: 10.3390/diagnostics13193030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 09/21/2023] [Accepted: 09/21/2023] [Indexed: 10/15/2023] Open
Abstract
Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system that prominently affects young adults due to its debilitating nature. The pathogenesis of the disease is focused on the inflammation and neurodegeneration processes. Inflammation is associated with relapses, while neurodegeneration emerges in the progressive stages of the disease. Magnetic resonance imaging (MRI) is commonly used for the diagnosis of MS, and guidelines such as the McDonald criteria are available. MRI is an essential tool to demonstrate the spatial distribution and changes over time in the disease. This study discusses the use of image processing techniques for the diagnosis of MS and specifically combines the MobileNetV2 network with exemplar-based learning, IMrMr feature selection, and K-Nearest Neighbors (KNN) classification methods. Experiments conducted on two different datasets (Dataset 1 and Dataset 2) demonstrate that these methods provide high accuracy in diagnosing MS. Dataset 1 comprises 128 patients with 706 MRI images, 131 MS patients with 667 MRI images, and 150 healthy control subjects with 1373 MRI images. Dataset 2 includes an MS group with 650 MRI images and a healthy control group with 676 MRI images. The results of the study include 10-fold cross-validation results performed on different image sections (Axial, Sagittal, and Hybrid) for Dataset 1. Accuracy rates of 99.76% for Axial, 99.48% for Sagittal, and 98.02% for Hybrid sections were achieved. Furthermore, 100% accuracy was achieved on Dataset 2. In conclusion, this study demonstrates the effective use of powerful image processing methods such as the MobileNetV2 network and exemplar-based learning for the diagnosis of MS. These findings suggest that these methods can be further developed in future research and offer significant potential for clinical applications in the diagnosis and monitoring of MS.
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Affiliation(s)
- Tuba Ekmekyapar
- Department of Neurology, Elazığ Fethi Sekin City Hospital, Elazig 23280, Turkiye
| | - Burak Taşcı
- Vocational School of Technical Sciences, Firat University, Elazig 23119, Turkiye
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Bhandary S, Kuhn D, Babaiee Z, Fechter T, Benndorf M, Zamboglou C, Grosu AL, Grosu R. Investigation and benchmarking of U-Nets on prostate segmentation tasks. Comput Med Imaging Graph 2023; 107:102241. [PMID: 37201475 DOI: 10.1016/j.compmedimag.2023.102241] [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: 11/30/2022] [Revised: 05/03/2023] [Accepted: 05/03/2023] [Indexed: 05/20/2023]
Abstract
In healthcare, a growing number of physicians and support staff are striving to facilitate personalized radiotherapy regimens for patients with prostate cancer. This is because individual patient biology is unique, and employing a single approach for all is inefficient. A crucial step for customizing radiotherapy planning and gaining fundamental information about the disease, is the identification and delineation of targeted structures. However, accurate biomedical image segmentation is time-consuming, requires considerable experience and is prone to observer variability. In the past decade, the use of deep learning models has significantly increased in the field of medical image segmentation. At present, a vast number of anatomical structures can be demarcated on a clinician's level with deep learning models. These models would not only unload work, but they can offer unbiased characterization of the disease. The main architectures used in segmentation are the U-Net and its variants, that exhibit outstanding performances. However, reproducing results or directly comparing methods is often limited by closed source of data and the large heterogeneity among medical images. With this in mind, our intention is to provide a reliable source for assessing deep learning models. As an example, we chose the challenging task of delineating the prostate gland in multi-modal images. First, this paper provides a comprehensive review of current state-of-the-art convolutional neural networks for 3D prostate segmentation. Second, utilizing public and in-house CT and MR datasets of varying properties, we created a framework for an objective comparison of automatic prostate segmentation algorithms. The framework was used for rigorous evaluations of the models, highlighting their strengths and weaknesses.
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Affiliation(s)
- Shrajan Bhandary
- Cyber-Physical Systems Division, Institute of Computer Engineering, Faculty of Informatics, Technische Universität Wien, Vienna, 1040, Austria.
| | - Dejan Kuhn
- Division of Medical Physics, Department of Radiation Oncology, Medical Center University of Freiburg, Freiburg, 79106, Germany; Faculty of Medicine, University of Freiburg, Freiburg, 79106, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, 79106, Germany
| | - Zahra Babaiee
- Cyber-Physical Systems Division, Institute of Computer Engineering, Faculty of Informatics, Technische Universität Wien, Vienna, 1040, Austria
| | - Tobias Fechter
- Division of Medical Physics, Department of Radiation Oncology, Medical Center University of Freiburg, Freiburg, 79106, Germany; Faculty of Medicine, University of Freiburg, Freiburg, 79106, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, 79106, Germany
| | - Matthias Benndorf
- Department of Diagnostic and Interventional Radiology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, 79106, Germany
| | - Constantinos Zamboglou
- Faculty of Medicine, University of Freiburg, Freiburg, 79106, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, 79106, Germany; Department of Radiation Oncology, Medical Center University of Freiburg, Freiburg, 79106, Germany; German Oncology Center, European University, Limassol, 4108, Cyprus
| | - Anca-Ligia Grosu
- Faculty of Medicine, University of Freiburg, Freiburg, 79106, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, 79106, Germany; Department of Radiation Oncology, Medical Center University of Freiburg, Freiburg, 79106, Germany
| | - Radu Grosu
- Cyber-Physical Systems Division, Institute of Computer Engineering, Faculty of Informatics, Technische Universität Wien, Vienna, 1040, Austria; Department of Computer Science, State University of New York at Stony Brook, NY, 11794, USA
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