2D Multi-Class Model for Gray and White Matter Segmentation of the Cervical Spinal Cord at 7T
Aix Marseille Univ（艾克斯-马赛大学）；CNRS（法国国家科学研究中心）；CRMBM（生物医学与医学影像学研究中心）；APHM（Assistance Publique - Hôpitaux de Marseille（马赛公立医院系统））；CHU Timone（蒂莫讷大学医院）；CEMEREM（马赛医学研究中心）；NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal（蒙特利尔理工大学生物医学工程系NeuroPoly实验室）；MILA - Québec AI Institute（魁北克人工智能研究所（MILA））
Nilser J. Laines Medina, Charley Gros, Julien Cohen-Adad, Virginie Callot, Arnaud Le Troter
The spinal cord (SC), which conveys information between the brain and the peripheral nervous system, plays a key role in various neurological disorders such as multiple sclerosis (MS) and amyotrophic lateral sclerosis (ALS), in which both gray matter (GM) and white matter (WM) may be impaired. While automated methods for WM/GM segmentation are now largely available, these techniques, developed for conventional systems (3T or lower) do not necessarily perform well on 7T MRI data, which feature finer details, contrasts, but also different artifacts or signal dropout.
The primary goal of this study is thus to propose a new deep learning model that allows robust SC/GM multi-class segmentation based on ultra-high resolution 7T T2*-w MR images. The second objective is to highlight the relevance of implementing a specific data augmentation (DA) strategy, in particular to generate a generic model that could be used for multi-center studies at 7T.
Novartis Pharmaceutical Company（诺华制药公司）；Big Data Institute, University of Oxford（牛津大学大数据研究所）
Manual annotation of vertebrae on spinal X-ray imaging is costly and time-consuming due to bone shape complexity and image quality variations. In this study, we address this challenge by proposing an ensemble method called VertXNet, to automatically segment and label vertebrae in X-ray spinal images. VertXNet combines two state-of-the-art segmentation models, namely U-Net and Mask R-CNN to improve vertebrae segmentation. A main feature of VertXNet is to also infer vertebrae labels thanks to its Mask R-CNN component (trained to detect 'reference' vertebrae) on a given spinal X-ray image. VertXN…
Rootlets-based registration to the spinal cord PAM50 template
Polytechnique Montreal（蒙特利尔理工学院）；Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；Palacký University Olomouc（帕拉茨基大学奥洛穆茨分校）；Stanford School of Medicine（斯坦福医学院）；Italian National Institute of Health（意大利国家卫生研究院）；Université de Montréal（蒙特利尔大学）；Centre de Recherche du CHU Sainte-Justine（圣贾斯汀大学医学中心研究中心）
Polytechnique Montreal（蒙特利尔理工学院）；Mila, Quebec AI Institute（米拉魁北克人工智能研究所）；Beijing Tiantan Hospital, Capital Medical University（首都医科大学附属北京天坛医院）；Université de Montréal（蒙特利尔大学）
Spinal cord tumors lead to neurological morbidity and mortality. Being able to obtain morphometric quantification (size, location, growth rate) of the tumor, edema, and cavity can result in improved monitoring and treatment planning. Such quantification requires the segmentation of these structures into three separate classes. However, manual segmentation of 3-dimensional structures is time-consuming and tedious, motivating the development of automated methods. Here, we tailor a model adapted to the spinal cord tumor segmentation task. Data were obtained from 343 patients using gadolinium-enha…
Towards contrast-agnostic soft segmentation of the spinal cord
NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal（神经Poly实验室，生物医学工程学院，蒙特利尔理工学院）；Mila - Québec Artificial Intelligence Institute（魁北克人工智能研究所）；Translational Imaging in Neurology (ThINK) Basel（神经学转化成像（ThINK）巴塞尔）；Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel（生物医学工程系，医学学院，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), 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）；Neuroradiology Department, Neurocenter of Southern Switzerland, Ospedale Regionale di Lugano（神经放射科，瑞士南部神经中心，卢加诺地区医院）；Department of Physical Medicine and Rehabilitation Physical Therapy Program, University of Colorado School of Medicine（康复医学系，科罗拉多大学医学院物理治疗项目）；Division of Pain Medicine, Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine（疼痛医学科，麻醉学、围手术期和疼痛医学部，斯坦福大学医学院）；Functional Neuroimaging Unit, CRIUGM, University of Montreal（功能神经影像单元，CRIUGM，蒙特利尔大学）
Obtaining high quality images of the spinal cord with MRI is difficult, partly due to the fact that the spinal cord is surrounded by a number of structures that have differing magnetic susceptibility. This causes inhomogeneities in the magnetic field, which in turn lead to image artifacts. In order to address this issue, linear compensation gradients can be employed. The latter can be generated using an MRI scanner's first order gradient coils and adjusted on a per-slice basis, in order to correct for through-plane ("z") magnetic field gradients. This approach is referred to as z-shimming. The…
Background: Magnetic field inhomogeneities generate important geometric distortions in reconstructed echo-planar images. Various procedures were proposed for correcting these distortions on brain images; yet, few neuroimaging studies tailored and incorporated the use of these techniques in spinal cord diffusion MRI. Purpose: We present a comparative evaluation of distortion correction methods that use the reversed gradient polarity technique on spinal cord. We propose novel geometric metrics to measure the alignment of the reconstructed diffusion model with the apparent centerline of the spina…
Pathology-Guided AI System for Accurate Segmentation and Diagnosis of Cervical Spondylosis
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT)（先进磁共振诊断与治疗技术国家工程研究中心）；Med-X Research Institute, Shanghai Jiao Tong University（Med-X研究院，上海交通大学）；Department of Spine Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属仁济医院脊柱外科部）；Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院放射科）；Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属仁济医院放射科）
Measuring hand use in the home after cervical spinal cord injury using egocentric video
KITE Research Institute, University Health Network（KITE研究所（大学健康网络））；The BioRobotics Institute, Scuola Superiore Sant’Anna（圣安娜高等学校生物机器人研究所）；Institute of Biomedical Engineering, University of Toronto（多伦多大学生物医学工程研究所）；Sunnybrook Research Institute, Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心桑尼布鲁克研究所）；Temerty Faculty of Medicine, University of Toronto（多伦多大学特默蒂医学院）；Brain and Spinal Cord Rehabilitation Program, Toronto Rehabilitation Institute – University Health Network（大学健康网络多伦多康复研究所脑与脊髓康复项目）；Department of Physical Therapy, University of Toronto（多伦多大学物理治疗系）；Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto（多伦多大学爱德华·S·罗杰斯 Sr. 电气与计算机工程系）
U2AD: Uncertainty-based Unsupervised Anomaly Detection Framework for Detecting T2 Hyperintensity in MRI Spinal Cord
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；Renji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属仁济医院）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT)（先进磁共振诊疗技术国家工程研究中心）；Med-X Research Institute, Shanghai Jiao Tong University（上海交通大学Med-X研究院）
SPINEPS -- Automatic Whole Spine Segmentation of T2-weighted MR images using a Two-Phase Approach to Multi-class Semantic and Instance Segmentation
Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar（伊萨尔河右岸医院）；University of Zurich（苏黎世大学）；Imperial College London（帝国理工学院）；Helmholtz Munich（亥姆霍兹慕尼黑中心）；University Medical Center Rostock（罗斯托克大学医学中心）；University of Freiburg（弗赖堡大学）；University Hospital Augsburg（奥格斯堡大学医院）；Augsburg University（奥格斯堡大学）
Optimization in Sparse 2D to Dense 3D Weakly Supervised Learning: Application to Multi-Label Segmentation of Large ex vivo MRI Data
NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal（神经多极实验室，生物医学工程学院，蒙特利尔理工学院）；École Centrale de Lyon（里昂中央理工学院）；Mila - Quebec AI Institute（魁北克人工智能研究所）；Functional Neuroimaging Unit, CRIUGM, University of Montreal（功能神经影像单元，CRIUGM，蒙特利尔大学）；Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health（转化神经放射学部门，国家神经疾病与中风研究所，国家卫生研究院）；Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel（神经学转化成像（ThINk）巴塞尔，生物医学工程系，医学院，巴塞尔大学医院和巴塞尔大学）；Neurologic Clinic and Policlinic, Departments of Medicine, University Hospital Basel, Switzerland（神经科诊所和多科诊所，医学院，巴塞尔大学医院，瑞士）；Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Switzerland（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，瑞士）；National Institute of Neurological Disorders and Stroke, National Institutes of Health（国家神经疾病与中风研究所，国家卫生研究院）；Centre de recherche du CHU Sainte-Justine, Université de Montréal, Montreal, QC, Canada（圣朱斯特医院研究中心，蒙特利尔大学，蒙特利尔，魁北克，加拿大）；Quantitative MRI core facility, NINDS, NIH（定量MRI核心设施，NINDS，NIH）；Experimental Immunotherapeutics Unit, Division of Neuroimmunology and Neurovirology, NINDS, NIH（实验免疫治疗单元，神经免疫学和神经病毒学部门，NINDS，NIH）
University of Zurich（苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）；Lausanne University Hospital（洛桑大学医院）；University of Lausanne（洛桑大学）；Technical University of Munich（慕尼黑工业大学）；Brain Research Institute, University of Zurich（苏黎世大学脑研究所）；University of Debrecen（德布勒森大学）
It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders. To facilitate this analysis, automatic multi-tissue fetal brain segmentation algorithms are needed, which in turn requires open databases of segmented fetal brains. Here we introduce a publicly available database of 50 manually segmented pathological and non-pathological fetal magnetic resonance brain volume reconstructions across a range of gestational ages (20 to 33 weeks) into 7 different tissue categories (extern…
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…
