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）
Nataliia Molchanova, Alessandro Cagol, Pedro M. Gordaliza, Mario Ocampo-Pineda, Po-Jui Lu, Matthias Weigel, Xinjie Chen, Adrien Depeursinge, Cristina Granziera,… 展开作者
Nataliia Molchanova, Alessandro Cagol, Pedro M. Gordaliza, Mario Ocampo-Pineda, Po-Jui Lu, Matthias Weigel, Xinjie Chen, Adrien Depeursinge, Cristina Granziera, Henning Müller, Meritxell Bach Cuadra
Uncertainty quantification (UQ) has become critical for evaluating the reliability of artificial intelligence systems, especially in medical image segmentation. This study addresses the interpretability of instance-wise uncertainty values in deep learning models for focal lesion segmentation in magnetic resonance imaging, specifically cortical lesion (CL) segmentation in multiple sclerosis. CL segmentation presents several challenges, including the complexity of manual segmentation, high variability in annotation, data scarcity, and class imbalance, all of which contribute to aleatoric and epistemic uncertainty. We explore how UQ can be used not only to assess prediction reliability but also to provide insights into model behavior, detect biases, and verify the accuracy of UQ methods. Our research demonstrates the potential of instance-wise uncertainty values to offer post hoc global model explanations, serving as a sanity check for the model. The implementation is available at https://github.com/NataliiaMolch/interpret-lesion-unc.
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…
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…
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））
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）
Boston Children's Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；Northeastern University（东北大学）
Fully convolutional deep neural networks have been asserted to be fast and precise frameworks with great potential in image segmentation. One of the major challenges in training such networks raises when data is unbalanced, which is common in many medical imaging applications such as lesion segmentation where lesion class voxels are often much lower in numbers than non-lesion voxels. A trained network with unbalanced data may make predictions with high precision and low recall, being severely biased towards the non-lesion class which is particularly undesired in most medical applications where…
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…
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）
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 …
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）
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）
Northeastern University（东北大学）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）
Fully convolutional deep neural networks carry out excellent potential for fast and accurate image segmentation. One of the main challenges in training these networks is data imbalance, which is particularly problematic in medical imaging applications such as lesion segmentation where the number of lesion voxels is often much lower than the number of non-lesion voxels. Training with unbalanced data can lead to predictions that are severely biased towards high precision but low recall (sensitivity), which is undesired especially in medical applications where false negatives are much less tolera…
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…
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）
Cornell University（康奈尔大学）；Weill Cornell Medical College（威尔康奈尔医学院）
Segmentation of anatomical regions of interest such as vessels or small lesions in medical images is still a difficult problem that is often tackled with manual input by an expert. One of the major challenges for this task is that the appearance of foreground (positive) regions can be similar to background (negative) regions. As a result, many automatic segmentation algorithms tend to exhibit asymmetric errors, typically producing more false positives than false negatives. In this paper, we aim to leverage this asymmetry and train a diverse ensemble of models with very high recall, while sacri…
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…
Robust and Generalisable Segmentation of Subtle Epilepsy-causing Lesions: a Graph Convolutional Approach
Helmholtz Center Munich（亥姆霍兹慕尼黑中心）；LMU Munich（慕尼黑大学）；UCL Great Ormond Street Institute for Child Health（伦敦大学学院大奥蒙德街儿童健康研究所）；King’s College London（伦敦国王学院）；Harvard Medical School（哈佛医学院）；Massachusetts Institute of Technology（麻省理工学院）；University College London（伦敦大学学院）
Exploiting XAI maps to improve MS lesion segmentation and detection in MRI
University Hospital Basel（巴塞尔大学医院）；University of Basel（巴塞尔大学）；Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB)（巴塞尔临床神经免疫学与神经科学研究中心）；HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland（瑞士西部高等专业学院瓦莱州瓦利斯分校）；CIBM Center for Biomedical Imaging（生物医学成像中心（CIBM））
本文提出从 MS 病灶显著性映射中提取放射组学特征，并训练逻辑回归区分 TP/FP，显著提升 3D U-Net 的 MRI 病灶分割检测性能。
Polytechnique Montreal（蒙特利尔理工学院）；Université de Montréal（蒙特利尔大学）
Most image segmentation algorithms are trained on binary masks formulated as a classification task per pixel. However, in applications such as medical imaging, this "black-and-white" approach is too constraining because the contrast between two tissues is often ill-defined, i.e., the voxels located on objects' edges contain a mixture of tissues. Consequently, assigning a single "hard" label can result in a detrimental approximation. Instead, a soft prediction containing non-binary values would overcome that limitation. We introduce SoftSeg, a deep learning training approach that takes advantag…
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）
Focal cortical dysplasia (FCD) is one of the most common epileptogenic lesions associated with cortical development malformations. However, the accurate detection of the FCD relies on the radiologist professionalism, and in many cases, the lesion could be missed. In this work, we solve the problem of automatic identification of FCD on magnetic resonance images (MRI). For this task, we improve recent methods of Deep Learning-based FCD detection and apply it for a dataset of 15 labeled FCD patients. The model results in the successful detection of FCD on 11 out of 15 subjects.
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）
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…
Univ. Bordeaux（波尔多大学）；Bordeaux INP（波尔多国立高等综合理工学院）；CNRS（法国国家科学研究中心）；LaBRI（波尔多计算机科学实验室）；IMB（波尔多数学研究所）；ITACA（信息与通信技术应用研究所）；Universitat Politècnica de València（瓦伦西亚理工大学）
Semi-supervised learning (SSL) uses unlabeled data to compensate for the scarcity of annotated images and the lack of method generalization to unseen domains, two usual problems in medical segmentation tasks. In this work, we propose POPCORN, a novel method combining consistency regularization and pseudo-labeling designed for image segmentation. The proposed framework uses high-level regularization to constrain our segmentation model to use similar latent features for images with similar segmentations. POPCORN estimates a proximity graph to select data from easiest ones to more difficult ones,…
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…
