Brain lesion volume measured on T2 weighted MRI images is a clinically important disease marker in multiple sclerosis (MS). Manual delineation of MS lesions is a time-consuming and highly operator-dependent task, which is influenced by lesion size, shape and conspicuity. Recently, automated lesion segmentation algorithms based on deep neural networks have been developed with promising results. In this paper, we propose a novel recurrent slice-wise attention network (RSANet), which models 3D MRI images as sequences of slices and captures long-range dependencies through a recurrent manner to utilize contextual information of MS lesions. Experiments on a dataset with 43 patients show that the proposed method outperforms the state-of-the-art approaches. Our implementation is available online at https://github.com/tinymilky/RSANet.
Technical University of Denmark（丹麦技术大学）；Athinoula A. Martinos Center for Biomedical Imaging（阿西努拉·A·马丁诺斯生物医学成像中心）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Technical University of Munich（慕尼黑工业大学）
In this paper we propose a novel method for the segmentation of longitudinal brain MRI scans of patients suffering from Multiple Sclerosis. The method builds upon an existing cross-sectional method for simultaneous whole-brain and lesion segmentation, introducing subject-specific latent variables to encourage temporal consistency between longitudinal scans. It is very generally applicable, as it does not make any prior assumptions on the scanner, the MRI protocol, or the number and timing of longitudinal follow-up scans. Preliminary experiments on three longitudinal datasets indicate that the …
University of Sydney（悉尼大学）；University of New South Wales（新南威尔士大学）；Sydney Neuroimaging Analysis Centre（悉尼神经影像分析中心）
Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central nervous system, characterized by the appearance of focal lesions in the white and gray matter that topographically correlate with an individual patient's neurological symptoms and signs. Magnetic resonance imaging (MRI) provides detailed in-vivo structural information, permitting the quantification and categorization of MS lesions that critically inform disease management. Traditionally, MS lesions have been manually annotated on 2D MRI slices, a process that is inefficient and prone to inter-/intra-obser…
SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints
Imperial College London（伦敦帝国学院）；Data Science Institute, Imperial College London（伦敦帝国学院数据科学研究所）；Beijing Institute of Technology（北京理工大学）；UK Dementia Research Institute（英国痴呆症研究所）；Rosalind Franklin Institute（罗莎琳德·富兰克林研究所）
Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images
University of Zurich（苏黎世大学）；Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学 rechts der Isar 医院）；Imperial College London（帝国理工学院）；The First Affiliated Hospital of Xi’an Jiaotong University（西安交通大学第一附属医院）
Istituto Italiano di Tecnologia（意大利理工学院）；University of Genoa（热那亚大学）；Fondation Campus Biotech Geneva（日内瓦校园生物技术基金会）；San Raffaele Scientific Institute（圣拉斐尔科学研究所）；University of Verona（维罗纳大学）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）
In this paper, we present an automated approach for segmenting multiple sclerosis (MS) lesions from multi-modal brain magnetic resonance images. Our method is based on a deep end-to-end 2D convolutional neural network (CNN) for slice-based segmentation of 3D volumetric data. The proposed CNN includes a multi-branch downsampling path, which enables the network to encode information from multiple modalities separately. Multi-scale feature fusion blocks are proposed to combine feature maps from different modalities at different stages of the network. Then, multi-scale feature upsampling blocks ar…
Polytechnique Montreal（蒙特利尔理工学院）；MILA, Quebec AI Institute（魁北克人工智能研究所MILA）；CHU Rennes（雷恩大学中心医院）；CHU Montpellier（蒙彼利埃大学中心医院）；Karolinska Institutet（卡罗林斯卡学院）；University of California, San Francisco（加州大学旧金山分校）；National Institute of Neurological Disorders and Stroke, National Institutes of Health（美国国立卫生研究院国家神经疾病与中风研究所）；IRCCS San Raffaele Scientific Institute（IRCCS圣拉斐尔科学研究所）；Vita-Salute San Raffaele University（圣拉斐尔生命健康大学）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；AP-HM, CHU Timone（马赛公共医院集团蒂莫内大学中心医院）；Aix-Marseille Univ（艾克斯-马赛大学）；CNRS（法国国家科学研究中心）
Segmentation of Multiple Sclerosis (MS) lesions is a challenging problem. Several deep-learning-based methods have been proposed in recent years. However, most methods tend to be static, that is, a single model trained on a large, specialized dataset, which does not generalize well. Instead, the model should learn across datasets arriving sequentially from different hospitals by building upon the characteristics of lesions in a continual manner. In this regard, we explore experience replay, a well-known continual learning method, in the context of MS lesion segmentation across multi-contrast d…
University of Girona（赫罗纳大学）；Assiut University（艾斯尤特大学）；Vall d’Hebron University Hospital（瓦尔德希伯伦大学医院）；Girona Magnetic Resonance Center（赫罗纳磁共振中心）；Dr. Josep Trueta University Hospital（何塞普·特鲁埃塔大学医院）
In recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images, due to their superior performance compared with those of other state-of-the-art methods. However, the accuracies of CNN methods tend to decrease significantly when evaluated on different image domains compared with those used for training, which demonstrates the lack of adaptability of CNNs to unseen imaging data. In this study, we analyzed the effect of intensity domain adaptation on our recently proposed CNN-based…
Multiple Sclerosis (MS) is a chronic neurological condition characterized by the development of lesions in the white matter of the brain. T2-fluid attenuated inversion recovery (FLAIR) brain magnetic resonance imaging (MRI) provides superior visualization and characterization of MS lesions, relative to other MRI modalities. Follow-up brain FLAIR MRI in MS provides helpful information for clinicians towards monitoring disease progression. In this study, we propose a novel modification to generative adversarial networks (GANs) to predict future lesion-specific FLAIR MRI for MS at fixed time inte…
Technical University of Munich（慕尼黑工业大学）；University of Ljubljana（卢布尔雅那大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Segmentation of Multiple Sclerosis (MS) lesions in longitudinal brain MR scans is performed for monitoring the progression of MS lesions. We hypothesize that the spatio-temporal cues in longitudinal data can aid the segmentation algorithm. Therefore, we propose a multi-task learning approach by defining an auxiliary self-supervised task of deformable registration between two time-points to guide the neural network toward learning from spatio-temporal changes. We show the efficacy of our method on a clinical dataset comprised of 70 patients with one follow-up study for each patient. Our results…
Flinders University（弗林德斯大学）；Flinders Medical Centre（弗林德斯医疗中心）；Dr Jones & Partners Medical Imaging（琼斯博士及合伙人医疗影像中心）；University of Oxford（牛津大学）；University of Adelaide（阿德莱德大学）
Longitudinal imaging forms an essential component in the management and follow-up of many medical conditions. The presence of lesion changes on serial imaging can have significant impact on clinical decision making, highlighting the important role for automated change detection. Lesion changes can represent anomalies in serial imaging, which implies a limited availability of annotations and a wide variety of possible changes that need to be considered. Hence, we introduce a new unsupervised anomaly detection and localisation method trained exclusively with serial images that do not contain any…
Istituto Italiano di Tecnologia（意大利理工学院）；Simon Fraser University（西蒙弗雷泽大学）；University of Verona（维罗纳大学）；Fondation Campus Biotech（生物科技园区基金会）；University of British Columbia（不列颠哥伦比亚大学）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）
This paper presents a simple and effective generalization method for magnetic resonance imaging (MRI) segmentation when data is collected from multiple MRI scanning sites and as a consequence is affected by (site-)domain shifts. We propose to integrate a traditional encoder-decoder network with a regularization network. This added network includes an auxiliary loss term which is responsible for the reduction of the domain shift problem and for the resulting improved generalization. The proposed method was evaluated on multiple sclerosis lesion segmentation from MRI data. We tested the proposed…
Lesion segmentation is a core task for quantitative analysis of MRI scans of Multiple Sclerosis patients. The recent success of deep learning techniques in a variety of medical image analysis applications has renewed community interest in this challenging problem and led to a burst of activity for new algorithm development. In this survey, we investigate the supervised CNN-based methods for MS lesion segmentation. We decouple these reviewed works into their algorithmic components and discuss each separately. For methods that provide evaluations on public benchmark datasets, we report compariso…
Hamburg University of Technology（汉堡工业大学）；jung diagnostics GmbH（jung diagnostics 有限公司）；University Hospital Zurich（苏黎世大学医院）；University of Zurich（苏黎世大学）；Technische Universität Dresden（德累斯顿工业大学）
Multiple sclerosis is an inflammatory autoimmune demyelinating disease that is characterized by lesions in the central nervous system. Typically, magnetic resonance imaging (MRI) is used for tracking disease progression. Automatic image processing methods can be used to segment lesions and derive quantitative lesion parameters. So far, methods have focused on lesion segmentation for individual MRI scans. However, for monitoring disease progression, \textit{lesion activity} in terms of new and enlarging lesions between two time points is a crucial biomarker. For this problem, several classic me…
University of Oulu（奥卢大学）；KFUPM（法赫德国王石油与矿业大学）；King Fahd Specialist Hospital Dammam（达曼法赫德国王专科医院）
Introduction: Multiple Sclerosis (MS) is a chronic disease that affects millions of people across the globe. MS can critically affect different organs of the central nervous system such as the eyes, the spinal cord, and the brain. Background: To help physicians in diagnosing MS lesions, computer-aided methods are widely used. In this regard, a considerable research has been carried out in the area of automatic detection and segmentation of MS lesions in magnetic resonance images (MRIs). Methodology: In this study, we review the different approaches that have been used in computer-aided detecti…
RimSet: Quantitatively Identifying and Characterizing Chronic Active Multiple Sclerosis Lesion on Quantitative Susceptibility Maps
Department of Electrical and Computer Engineering, Johns Hopkins University（约翰霍普金斯大学电气与计算机工程系）；Department of Radiology, Weill Medical College of Cornell University（康奈尔大学韦尔医学院放射科）；Sibley School of Mechanical and Aerospace Engineering, Cornell University（康奈尔大学西尔伯学校机械与航空航天工程系）；Department of Electrical and Computer Engineering, Cornell University（康奈尔大学电气与计算机工程系）
Institute of Medical Technology, Hamburg University of Technology（汉堡工业大学医学技术研究所）；jung diagnostics GmbH（荣格诊断有限公司）；Department of Neurology, University Hospital Zurich and University of Zurich（苏黎世大学医院与苏黎世大学神经病学系）
Multiple sclerosis lesion activity segmentation is the task of detecting new and enlarging lesions that appeared between a baseline and a follow-up brain MRI scan. While deep learning methods for single-scan lesion segmentation are common, deep learning approaches for lesion activity have only been proposed recently. Here, a two-path architecture processes two 3D MRI volumes from two time points. In this work, we investigate whether extending this problem to full 4D deep learning using a history of MRI volumes and thus an extended baseline can improve performance. For this purpose, we design a…
Novel structural-scale uncertainty measures and error retention curves: application to multiple sclerosis
Lausanne University Hospital（洛桑大学医院）；University of Lausanne（洛桑大学）；University of Applied Sciences of Western Switzerland（瑞士西部应用科学大学）；University of Cambridge（剑桥大学）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；University Hospital Basel（巴塞尔大学医院）；IBM Research Europe（IBM欧洲研究院）
McGill University（麦吉尔大学）；MILA (Quebec Artificial Intelligence Institute)（魁北克人工智能研究所（MILA））；University of Edinburgh（爱丁堡大学）；The Alan Turing Institute（阿兰·图灵研究所）；NeuroRx Research（NeuroRx研究公司）
Many automatic machine learning models developed for focal pathology (e.g. lesions, tumours) detection and segmentation perform well, but do not generalize as well to new patient cohorts, impeding their widespread adoption into real clinical contexts. One strategy to create a more diverse, generalizable training set is to naively pool datasets from different cohorts. Surprisingly, training on this \it{big data} does not necessarily increase, and may even reduce, overall performance and model generalizability, due to the existence of cohort biases that affect label distributions. In this paper,…
Interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis
University of Lausanne（洛桑大学）；Lausanne University Hospital（洛桑大学医院）；University of Applied Sciences Western Switzerland (HES-SO)（瑞士西部应用科学大学（HES-SO））；CIBM Center for Biomedical Imaging（CIBM生物医学成像中心）；University Hospital and University Basel（巴塞尔大学医院与巴塞尔大学）；University of Genova（热那亚大学）；University of Geneva（日内瓦大学）
Multiple Sclerosis (MS) is a chronic neuroinflammatory disease and multi-modality MRIs are routinely used to monitor MS lesions. Many automatic MS lesion segmentation models have been developed and have reached human-level performance. However, most established methods assume the MRI modalities used during training are also available during testing, which is not guaranteed in clinical practice. Previously, a training strategy termed Modality Dropout (ModDrop) has been applied to MS lesion segmentation to achieve the state-of-the-art performance with missing modality. In this paper, we present …
Imperial College London（帝国理工学院）；UK Dementia Research Institute（英国痴呆症研究所）
Understanding the intensity characteristics of brain lesions is key for defining image-based biomarkers in neurological studies and for predicting disease burden and outcome. In this work, we present a novel foreground-based generative method for modelling the local lesion characteristics that can both generate synthetic lesions on healthy images and synthesize subject-specific pseudo-healthy images from pathological images. Furthermore, the proposed method can be used as a data augmentation module to generate synthetic images for training brain image segmentation networks. Experiments on mult…
Polytechnique Montreal（蒙特利尔理工学院）；University of Montreal（蒙特利尔大学）；CRIUGM（蒙特利尔大学神经科学研究所）
Despite recent improvements in medical image segmentation, the ability to generalize across imaging contrasts remains an open issue. To tackle this challenge, we implement Feature-wise Linear Modulation (FiLM) to leverage physics knowledge within the segmentation model and learn the characteristics of each contrast. Interestingly, a well-optimised U-Net reached the same performance as our FiLMed-Unet on a multi-contrast dataset (0.72 of Dice score), which suggests that there is a bottleneck in spinal MS lesion segmentation different from the generalization across varying contrasts. This bottle…
Univ. Bordeaux（波尔多大学）；Bordeaux INP（波尔多国立理工学院）；CNRS（法国国家科学研究中心）；INSERM（法国国家健康与医学研究院）；Neurocentre Magendie（马让迪神经中心）；Universitat Politècnica de València（瓦伦西亚理工大学）
Recently, segmentation methods based on Convolutional Neural Networks (CNNs) showed promising performance in automatic Multiple Sclerosis (MS) lesions segmentation. These techniques have even outperformed human experts in controlled evaluation conditions such as Longitudinal MS Lesion Segmentation Challenge (ISBI Challenge). However state-of-the-art approaches trained to perform well on highly-controlled datasets fail to generalize on clinical data from unseen datasets. Instead of proposing another improvement of the segmentation accuracy, we propose a novel method robust to domain shift and p…
Integrating Radiomics with Deep Learning Enhances Multiple Sclerosis Lesion Delineation
Skolkovo Institute of Science and Technology（斯克尔科夫科学与技术研究所）；Biomedically Informed Artificial Intelligence Laboratory（生物医学引导人工智能实验室）；University of Sharjah（沙迦大学）；College of Medicine and Health Sciences（医学与健康科学学院）；United Arab Emirates University（阿拉伯联合酋长国大学）
University of LAquila（拉奎拉大学）；Sapienza University of Rome（罗马第一大学）
To date, several automated strategies for identification/segmentation of Multiple Sclerosis (MS) lesions with the use of Magnetic Resonance Imaging (MRI) have been presented, but they are outperformed by human experts, from whom they act very differently. This is mainly due to: the ambiguity originated by MRI instabilities; peculiar variability of MS; non specificity of MRI regarding MS. Physicians partially manage the uncertainty generated by ambiguity relying on radiological/clinical/anatomical background and experience. To emulate human diagnosis, we present an automated framework for ident…
Amirkabir University of Technology (Tehran Polytechnic)（阿米尔卡比尔理工大学（德黑兰理工学院））；South Tehran Branch Islamic Azad University (IAU)（伊斯兰阿扎德大学南德黑兰分校）；Arak University of Medical Sciences（阿拉克医科大学）
Magnetic resonance images (MRI) play an important role in supporting and substituting clinical information in the diagnosis of multiple sclerosis (MS) disease by presenting lesion in brain MR images. In this paper, an algorithm for MS lesion segmentation from Brain MR Images has been presented. We revisit the modification of properties of fuzzy -c means algorithms and the canny edge detection. By changing and reformed fuzzy c-means clustering algorithms, and applying canny contraction principle, a relationship between MS lesions and edge detection is established. For the special case of FCM, w…
McGill University（麦吉尔大学）；MILA Quebec AI Institute（魁北克人工智能研究所（MILA））；Montreal Neurological Institute（蒙特利尔神经学研究所）
Precision medicine for chronic diseases such as multiple sclerosis (MS) involves choosing a treatment which best balances efficacy and side effects/preferences for individual patients. Making this choice as early as possible is important, as delays in finding an effective therapy can lead to irreversible disability accrual. To this end, we present the first deep neural network model for individualized treatment decisions from baseline magnetic resonance imaging (MRI) (with clinical information if available) for MS patients. Our model (a) predicts future new and enlarging T2 weighted (NE-T2) le…
UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentation
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA（电气与计算机工程系，约翰霍普金斯大学）；Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37215, USA（电气与计算机工程系，范德比尔特大学）；Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA（神经病学系，约翰霍普金斯医学院）；Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA（计算机科学系，约翰霍普金斯大学）；Department of Radiology, Uniformed Services University of the Health Sciences, Bethesda, MD, 20814, USA（放射学系，统一服务大学健康科学学院）
Objective: Multiple Sclerosis (MS) is an autoimmune, and demyelinating disease that leads to lesions in the central nervous system. This disease can be tracked and diagnosed using Magnetic Resonance Imaging (MRI). Up to now a multitude of multimodality automatic biomedical approaches is used to segment lesions which are not beneficial for patients in terms of cost, time, and usability. The authors of the present paper propose a method employing just one modality (FLAIR image) to segment MS lesions accurately. Methods: A patch-based Convolutional Neural Network (CNN) is designed, inspired by 3D…
Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；University of Lausanne（洛桑大学）；Lausanne University Hospital（洛桑大学医院）；National Institute of Neurological Disorders and Stroke（美国国家神经疾病与卒中研究所）；National Institutes of Health（美国国立卫生研究院）；University of Bern（伯尔尼大学）；University of Pennsylvania（宾夕法尼亚大学）；Cedars-Sinai Medical Center（西达赛奈医疗中心）
The automated detection of cortical lesions (CLs) in patients with multiple sclerosis (MS) is a challenging task that, despite its clinical relevance, has received very little attention. Accurate detection of the small and scarce lesions requires specialized sequences and high or ultra-high field MRI. For supervised training based on multimodal structural MRI at 7T, two experts generated ground truth segmentation masks of 60 patients with 2014 CLs. We implemented a simplified 3D U-Net with three resolution levels (3D U-Net-). By increasing the complexity of the task (adding brain tissue segmen…
Technical University of Denmark（丹麦技术大学）；Copenhagen University Hospital Hvidovre（哥本哈根大学医院维兹奥勒医院）；Medical Image Analysis Center (MIAC AG)（医学图像分析中心（MIAC AG））；Technical University of Munich（慕尼黑工业大学）；Copenhagen University Hospital Bispebjerg（哥本哈根大学医院比斯佩比约医院）；University of Copenhagen（哥本哈根大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients. The method integrates a novel model for white matter lesions into a previously validated generative model for whole-brain segmentation. By using separate models for the shape of anatomical structures and their appearance in MRI, the algorithm can adapt to data acquired with different scanners and imaging protocols without retraining. We validate the method using four disparate datasets, showing rob…
Faculty of Biology and Medicine, University of Lausanne (UNIL)（日内瓦大学生物医学学院）；Radiology Department, Lausanne University Hospital (CHUV)（拉索恩大学医院放射科）；MedGIFT, Institute of Informatics, School of Management, HES--SO Valais--Wallis University of Applied Sciences and Arts Western Switzerland（应用科学与艺术西瓦利斯-瓦利斯大学信息学院）；CIBM Center for Biomedical Imaging（生物医学成像中心）；Department of Radiology and Medical Informatics, University of Geneva（日内瓦大学放射科与医学信息学系）；Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel（神经学转化成像（ThINK）巴塞尔，医学与生物医学工程系，巴塞尔大学医院和巴塞尔大学）；Multiple Sclerosis Center, Department of Neurology, University Hospital Basel（多发性硬化中心，神经科，巴塞尔大学医院）；Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel（临床神经免疫学和神经科学巴塞尔研究中心（RC2NB），巴塞尔大学医院和巴塞尔大学）；Department of Neurology, Icahn School of Medicine at Mount Sinai（伊坎医学院 Mount Sinai 神经科）；Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health（神经学转化放射学部门，国家神经疾病与中风研究所，国家卫生研究院）；Neuroinflammation Imaging Lab (NIL), Université catholique de Louvain（神经炎症成像实验室（NIL），卢瓦纳大学）；EPFL+ECAL Lab, École polytechnique fédérale de Lausanne (EPFL)（EPFL+ECAL 实验室，日内瓦联邦理工学院（EPFL））
QSM-RimDS: A detection and segmentation tool for paramagnetic rim lesions in multiple sclerosis
Weill Cornell Medicine（威尔康奈尔医学院）；Cornell University（康奈尔大学）；Yale University（耶鲁大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；University of Engineering and Technology, VNU（越南国家大学工程技术大学）
Benchmarking and Explaining Deep Learning Cortical Lesion MRI Segmentation in Multiple Sclerosis
Faculty of Biology and Medicine, University of Lausanne (UNIL)（日内瓦大学生物与医学学院）；Radiology Department, Lausanne University Hospital (CHUV)（洛桑大学医院放射科）；MedGIFT, Institute of Informatics, School of Management, HES--SO Valais--Wallis University of Applied Sciences（应用科学与艺术西瓦利斯-瓦利斯大学信息学院）；CIBM Center for Biomedical Imaging（生物医学成像中心）；Department of Radiology and Medical Informatics, University of Geneva（日内瓦大学放射科与医学信息学系）；Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel（神经学转化成像（ThINK）巴塞尔，巴塞尔大学医院和巴塞尔大学医学与生物医学工程系）；Multiple Sclerosis Center, Department of Neurology, University Hospital Basel（多发性硬化中心，巴塞尔大学医院神经科）；Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel（临床神经免疫学与神经科学巴塞尔研究中心（RC2NB），巴塞尔大学医院和巴塞尔大学）；Division of Radiological Physics, Department of Radiology, University Hospital Basel（放射物理学系，放射科，巴塞尔大学医院）；Department of Neurology, Icahn School of Medicine at Mount Sinai（伊坎医学院神经科）；Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health（神经疾病国家研究所转化神经放射学组，国家卫生研究院）；Neuroinflammation Imaging Lab (NIL), Université catholique de Louvain（神经炎症成像实验室（NIL），卢旺达大学）
Instance-level quantitative saliency in multiple sclerosis lesion segmentation
Translational Imaging in Neurology (ThINk)（神经学转化成像（ThINk））；Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel（医学与生物医学工程系，巴塞尔大学医院和巴塞尔大学）；Department of Neurology, University Hospital Basel（神经病学系，巴塞尔大学医院）；Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB)（巴塞尔临床神经免疫学和神经科学研究中心（RC2NB））；MedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland（MedGIFT，信息学院，管理学院，瓦莱-迪-瓦尔省应用科学与艺术西方瑞士大学）；CIBM Center for Biomedical Imaging, Lausanne, Switzerland（洛桑瑞士生物医学成像中心）；Radiology Department, Lausanne University Hospital (CHUV) and University of Lausanne（放射科，洛桑大学医院（CHUV）和洛桑大学）
Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；CIBM Center for Biomedical Imaging（CIBM生物医学影像中心）；UCLouvain（鲁汶大学）；Lausanne University and University Hospital（洛桑大学及大学医院）；National Institute of Neurological Disorders and Stroke, National Institutes of Health（美国国立卫生研究院国家神经疾病与卒中研究所）；Cedars-Sinai Medical Center（西达赛奈医疗中心）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Siemens Healthcare AG（西门子医疗股份公司）；Cliniques universitaires Saint-Luc, Université catholique de Louvain（圣吕克大学医院，天主教鲁汶大学）；CHUV（洛桑大学医院）；University of Pennsylvania（宾夕法尼亚大学）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；University Hospital Basel and University of Basel（巴塞尔大学医院及巴塞尔大学）；IRCCS San Raffaele Hospital and Vita-Salute San Raffaele University（圣拉斐尔医院及圣拉斐尔生命健康大学）；Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）
The current multiple sclerosis (MS) diagnostic criteria lack specificity, and this may lead to misdiagnosis, which remains an issue in present-day clinical practice. In addition, conventional biomarkers only moderately correlate with MS disease progression. Recently, advanced MS lesional imaging biomarkers such as cortical lesions (CL), the central vein sign (CVS), and paramagnetic rim lesions (PRL), visible in specialized magnetic resonance imaging (MRI) sequences, have shown higher specificity in differential diagnosis. Moreover, studies have shown that CL and PRL are potential prognostic bi…
Multiple Sclerosis (MS) is a chronic progressive neurological disease characterized by the development of lesions in the white matter of the brain. T2-fluid-attenuated inversion recovery (FLAIR) brain magnetic resonance imaging (MRI) provides superior visualization and characterization of MS lesions, relative to other MRI modalities. Longitudinal brain FLAIR MRI in MS, involving repetitively imaging a patient over time, provides helpful information for clinicians towards monitoring disease progression. Predicting future whole brain MRI examinations with variable time lag has only been attempte…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Automated segmentation of multiple sclerosis (MS) lesions from MRI scans is important to quantify disease progression. In recent years, convolutional neural networks (CNNs) have shown top performance for this task when a large amount of labeled data is available. However, the accuracy of CNNs suffers when dealing with few and/or sparsely labeled datasets. A potential solution is to leverage the information available in large public datasets in conjunction with a target dataset which only has limited labeled data. In this paper, we propose a training framework, SSL2 (self-supervised-semi-superv…
Learning from spatially inhomogenous data: resolution-adaptive convolutions for multiple sclerosis lesion segmentation
University Institute of Diagnostic and Interventional Neuroradiology（诊断与介入神经放射学大学研究所）；Inselspital, Bern University Hospital and University of Bern（伯尔尼大学医院因塞尔spital与伯尔尼大学）；Centre for Artificial Intelligence in Medicine（医学人工智能中心）；University of Bern（伯尔尼大学）
Centre for Intelligent Machines（智能机器中心）；McGill University（麦吉尔大学）；MILA（蒙特利尔学习算法研究所）；Montreal Neurological Institute（蒙特利尔神经学研究所）
There are many clinical contexts which require accurate detection and segmentation of all focal pathologies (e.g. lesions, tumours) in patient images. In cases where there are a mix of small and large lesions, standard binary cross entropy loss will result in better segmentation of large lesions at the expense of missing small ones. Adjusting the operating point to accurately detect all lesions generally leads to oversegmentation of large lesions. In this work, we propose a novel reweighing strategy to eliminate this performance gap, increasing small pathology detection performance while maint…
Polytechnique Montréal（蒙特利尔理工学院）；MILA - Québec AI Institute（MILA - 魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）
This paper gives a detailed description of the pipelines used for the 2nd edition of the MICCAI 2021 Challenge on Multiple Sclerosis Lesion Segmentation. An overview of the data preprocessing steps applied is provided along with a brief description of the pipelines used, in terms of the architecture and the hyperparameters. Our code for this work can be found at: https://github.com/ivadomed/ms-challenge-2021.
The University of Sydney（悉尼大学）；Sydney Neuroimaging Analysis Centre（悉尼神经影像分析中心）；NVIDIA Corporation（英伟达公司）
Federated learning (FL) has been widely employed for medical image analysis to facilitate multi-client collaborative learning without sharing raw data. Despite great success, FL's performance is limited for multiple sclerosis (MS) lesion segmentation tasks, due to variance in lesion characteristics imparted by different scanners and acquisition parameters. In this work, we propose the first FL MS lesion segmentation framework via two effective re-weighting mechanisms. Specifically, a learnable weight is assigned to each local node during the aggregation process, based on its segmentation perfo…
