Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；Helmholtz Information and Data Science School for Health（亥姆霍兹健康信息与数据科学学院）；Helmholtz Imaging（亥姆霍兹影像中心）
Maximilian Rokuss, Yannick Kirchhoff, Saikat Roy, Balint Kovacs, Constantin Ulrich, Tassilo Wald, Maximilian Zenk, Stefan Denner, Fabian Isensee, Philipp Vollmu… 展开作者
Maximilian Rokuss, Yannick Kirchhoff, Saikat Roy, Balint Kovacs, Constantin Ulrich, Tassilo Wald, Maximilian Zenk, Stefan Denner, Fabian Isensee, Philipp Vollmuth, Jens Kleesiek, Klaus Maier-Hein
Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level $F_1$ score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at www.github.com/MIC-DKFZ/Longitudinal-Difference-Weighting.
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 …
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…
New Jersey Institute of Technology（新泽西理工学院）；Kessler Foundation（凯斯勒基金会）；Emory University（埃默里大学）；Atlanta VA Medical Center（亚特兰大退伍军人事务医疗中心）；Medical College of Wisconsin（威斯康星医学院）；Rutgers University - Newark（罗格斯大学纽瓦克分校）
We propose a fully 3D multi-path convolutional network to predict stroke lesions from 3D brain MRI images. Our multi-path model has independent encoders for different modalities containing residual convolutional blocks, weighted multi-path feature fusion from different modalities, and weighted fusion modules to combine encoder and decoder features. Compared to existing 3D CNNs like DeepMedic, 3D U-Net, and AnatomyNet, our networks achieves the highest statistically significant cross-validation accuracy of 60.5% on the large ATLAS benchmark of 220 patients. We also test our model on multi-modal…
Promptable Longitudinal Lesion Segmentation in Whole-Body CT
German Cancer Research Center (DKFZ) Heidelberg（德国癌症研究中心（DKFZ）海德堡）；Division of Medical Image Computing（医学影像计算 division）；HIDSS4Health - Helmholtz Information and Data Science School for Health（HIDSS4Health - 赫尔姆霍茨信息与数据科学健康学校）；Faculty of Mathematics and Computer Science（数学与计算机科学学院）；Heidelberg University（海德堡大学）；Helmholtz Imaging（赫尔姆霍茨成像）；Pattern Analysis and Learning Group（模式分析与学习小组）；Department of Radiation Oncology（放射肿瘤科）
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…
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…
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）；Universitat Politècnica de València（瓦伦西亚理工大学）；INSERM（法国国家健康与医学研究院）；Neurocentre Magendie（马让迪神经中心）
The detection of new multiple sclerosis (MS) lesions is an important marker of the evolution of the disease. The applicability of learning-based methods could automate this task efficiently. However, the lack of annotated longitudinal data with new-appearing lesions is a limiting factor for the training of robust and generalizing models. In this work, we describe a deep-learning-based pipeline addressing the challenging task of detecting and segmenting new MS lesions. First, we propose to use transfer-learning from a model trained on a segmentation task using single time-points. Therefore, we …
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）
KU Leuven（鲁汶大学）；icometrix（伊科梅特里克斯公司）；Technical University of Denmark（丹麦技术大学）
The evaluation of white matter lesion progression is an important biomarker in the follow-up of MS patients and plays a crucial role when deciding the course of treatment. Current automated lesion segmentation algorithms are susceptible to variability in image characteristics related to MRI scanner or protocol differences. We propose a model that improves the consistency of MS lesion segmentations in inter-scanner studies. First, we train a CNN base model to approximate the performance of icobrain, an FDA-approved clinically available lesion segmentation software. A discriminator model is then…
Registration of Longitudinal Spine CTs for Monitoring Lesion Growth
Harvard Medical School（哈佛医学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Brigham and Women’s Hospital（布莱根妇女医院）；Isomics Inc（伊索米克斯公司）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）；Kharkevich Institute for Information Transmission Problems（哈尔克维奇信息传输问题研究所）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Higher School of Economics（高等经济学院）；Moscow Gamma-Knife Center（莫斯科伽马刀中心）；Burdenko Neurosurgery Institute（布尔坚科神经外科研究所）
Target imbalance affects the performance of recent deep learning methods in many medical image segmentation tasks. It is a twofold problem: class imbalance - positive class (lesion) size compared to negative class (non-lesion) size; lesion size imbalance - large lesions overshadows small ones (in the case of multiple lesions per image). While the former was addressed in multiple works, the latter lacks investigation. We propose a loss reweighting approach to increase the ability of the network to detect small lesions. During the learning process, we assign a weight to every image voxel. The as…
An early effective screening and grading of COVID-19 has become imperative towards optimizing the limited available resources of the medical facilities. An automated segmentation of the infected volumes in lung CT is expected to significantly aid in the diagnosis and care of patients. However, an accurate demarcation of lesions remains problematic due to their irregular structure and location(s) within the lung. A novel deep learning architecture, Composite Deep network with Feature Weighting (CDNetFW), is proposed for efficient delineation of infected regions from lung CT images. Initially a …
The reliable and timely stratification of bone lesion evolution risk in smoldering Multiple Myeloma plays an important role in identifying prime markers of the disease's advance and in improving the patients' outcome. In this work we provide an asymmetric cascade network for the longitudinal prediction of future bone lesions for T1 weighted whole body MR images. The proposed cascaded architecture, consisting of two distinct configured U-Nets, first detects the bone regions and subsequently predicts lesions within bones in a patch based way. The algorithm provides a full volumetric risk score m…
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…
PAII Inc.（PAII公司）；Ping An Technology（平安科技）；Beijing United Family Hospital（北京和睦家医院）；Chang Gung Memorial Hospital（长庚纪念医院）
Accurately segmenting a variety of clinically significant lesions from whole body computed tomography (CT) scans is a critical task on precision oncology imaging, denoted as universal lesion segmentation (ULS). Manual annotation is the current clinical practice, being highly time-consuming and inconsistent on tumor's longitudinal assessment. Effectively training an automatic segmentation model is desirable but relies heavily on a large number of pixel-wise labelled data. Existing weakly-supervised segmentation approaches often struggle with regions nearby the lesion boundaries. In this paper, …
University of Electronic Science and Technology of China（电子科技大学）；University of Maryland School of Medicine（马里兰大学医学院）；Rutgers, the State University of New Jersey（罗格斯大学（新泽西州立大学））
Computed Tomography (CT) plays an important role in monitoring radiation-induced Pulmonary Fibrosis (PF), where accurate segmentation of the PF lesions is highly desired for diagnosis and treatment follow-up. However, the task is challenged by ambiguous boundary, irregular shape, various position and size of the lesions, as well as the difficulty in acquiring a large set of annotated volumetric images for training. To overcome these problems, we propose a novel convolutional neural network called PF-Net and incorporate it into a semi-supervised learning framework based on Iterative Confidence-…
Simultaneous column-based deep learning progression analysis of atrophy associated with AMD in longitudinal OCT studies
School of Computer Science and Engineering, The Hebrew University of Jerusalem（耶路撒冷希伯来大学计算机科学与工程学院）；The Alexander Grass Center for Bioengineering, The Hebrew University of Jerusalem（耶路撒冷希伯来大学亚历山大·格拉斯生物工程中心）；Hadassah Medical Center（哈达萨医疗中心）
Automatically measuring lesion/tumor size with RECIST (Response Evaluation Criteria In Solid Tumors) diameters and segmentation is important for computer-aided diagnosis. Although it has been studied in recent years, there is still space to improve its accuracy and robustness, such as (1) enhancing features by incorporating rich contextual information while keeping a high spatial resolution and (2) involving new tasks and losses for joint optimization. To reach this goal, this paper proposes a transformer-based network (MeaFormer, Measurement transFormer) for lesion RECIST diameter prediction …
Ecole Centrale de Nantes（南特中央理工学院）；Keosys Medical Imaging（Keosys医学影像公司）；University of Nantes（南特大学）；University of Angers（昂热大学）；ICO Cancer Center（ICO癌症中心）
Longitudinal image registration is challenging and has not yet benefited from major performance improvements thanks to deep-learning. Inspired by Deep Image Prior, this paper introduces a different use of deep architectures as regularizers to tackle the image registration question. We propose a subject-specific deformable registration method called MIRRBA, relying on a deep pyramidal architecture to be the prior parametric model constraining the deformation field. Diverging from the supervised learning paradigm, MIRRBA does not require a learning database, but only the pair of images to be reg…
P-Count: Persistence-based Counting of White Matter Hyperintensities in Brain MRI
Martinos Center for Biomedical Imaging, MGH and Harvard Medical School（马蒂诺斯生物医学成像中心，麻省总医院与哈佛医学院）；Center for Medical Image Computing, University College London（伦敦大学学院医学影像计算中心）；Amsterdam University Medical Center（阿姆斯特丹大学医学中心）；Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital（哥本哈根大学医院丹麦磁共振研究中心）；Computer Science and Artificial Intelligence Laboratory, MIT（麻省理工学院计算机科学与人工智能实验室）
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Features learned from single radiologic images are unable to provide information about whether and how much a lesion may be changing over time. Time-dependent features computed from repeated images can capture those changes and help identify malignant lesions by their temporal behavior. However, longitudinal medical imaging presents the unique challenge of sparse, irregular time intervals in data acquisition. While self-attention has been shown to be a versatile and efficient learning mechanism for time series and natural images, its potential for interpreting temporal distance between sparse,…
Brain tumor imaging has been part of the clinical routine for many years to perform non-invasive detection and grading of tumors. Tumor segmentation is a crucial step for managing primary brain tumors because it allows a volumetric analysis to have a longitudinal follow-up of tumor growth or shrinkage to monitor disease progression and therapy response. In addition, it facilitates further quantitative analysis such as radiomics. Deep learning models, in particular CNNs, have been a methodology of choice in many applications of medical image analysis including brain tumor segmentation. In this …
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…
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）
How We Won the ISLES'24 Challenge by Preprocessing
Department of Mechanical Engineering, University of Washington（华盛顿大学机械工程系）；Paul G. Allen School of Computer Science, University of Washington（华盛顿大学保罗·G·阿伦计算机科学学院）；Department of Bioengineering, University of Washington（华盛顿大学生物工程系）
Open spina bifida (SB) is one of the most common congenital defects and can lead to impaired brain development. Emerging fetal surgery methods have shown considerable success in the treatment of patients with this severe anomaly. Afterwards, alterations in the brain development of these fetuses have been observed. Currently no longitudinal studies exist to show the effect of fetal surgery on brain development. In this work, we present a fetal MRI neuro-imaging analysis pipeline for fetuses with SB, including automated fetal ventricle segmentation and deformation-based morphometry, and demonstr…
Alzahra University（阿尔扎赫拉大学）；The University of Winnipeg（温尼伯大学）；Shahid Beheshti University of Medical Science（沙希德·贝赫什提医科大学）
The automatic analysis of subtle changes between longitudinal MR images is an important task as it is still a challenging issue in scope of the breast medical image processing. In this paper we propose an effective automatic change detection framework composed of two phases since previously used methods have features with low distinctive power. First, in the preprocessing phase an intensity normalization method is suggested based on Hierarchical Histogram Matching (HHM) that is more robust to noise than previous methods. To eliminate undesirable changes and extract the regions containing signi…
