Whole brain segmentation on a structural magnetic resonance imaging (MRI) is essential in non-invasive investigation for neuroanatomy. Historically, multi-atlas segmentation (MAS) has been regarded as the de facto standard method for whole brain segmentation. Recently, deep neural network approaches have been applied to whole brain segmentation by learning random patches or 2D slices. Yet, few previous efforts have been made on detailed whole brain segmentation using 3D networks due to the following challenges: (1) fitting entire whole brain volume into 3D networks is restricted by the current GPU memory, and (2) the large number of targeting labels (e.g., > 100 labels) with limited number of training 3D volumes (e.g., < 50 scans). In this paper, we propose the spatially localized atlas network tiles (SLANT) method to distribute multiple independent 3D fully convolutional networks to cover overlapped sub-spaces in a standard atlas space. This strategy simplifies the whole brain learning task to localized sub-tasks, which was enabled by combing canonical registration and label fusion techniques with deep learning. To address the second challenge, auxiliary labels on 5111 initially unlabeled scans were created by MAS for pre-training. From empirical validation, the state-of-the-art MAS method achieved mean Dice value of 0.76, 0.71, and 0.68, while the proposed method achieved 0.78, 0.73, and 0.71 on three validation cohorts. Moreover, the computational time reduced from > 30 hours using MAS to ~15 minutes using the proposed method. The source code is available online https://github.com/MASILab/SLANTbrainSeg
Detailed whole brain segmentation is an essential quantitative technique, which provides a non-invasive way of measuring brain regions from a structural magnetic resonance imaging (MRI). Recently, deep convolution neural network (CNN) has been applied to whole brain segmentation. However, restricted by current GPU memory, 2D based methods, downsampling based 3D CNN methods, and patch-based high-resolution 3D CNN methods have been the de facto standard solutions. 3D patch-based high resolution methods typically yield superior performance among CNN approaches on detailed whole brain segmentation…
University of California, San Diego（加利福尼亚大学圣地亚哥分校）；Cold Spring Harbor Laboratory（冷泉港实验室）；Department of Computer Science and Engineering, University of California, San Diego（加利福尼亚大学圣地亚哥分校计算机科学与工程系）
While modern imaging technologies such as fMRI have opened exciting new possibilities for studying the brain in vivo, histological sections remain the best way to study the anatomy of the brain at the level of single neurons. The histological atlas changed little since 1909 and localizing brain regions is a still a labor intensive process performed only by experienced neuro-anatomists. Existing digital atlases such as the Allen Brain atlas are limited to low resolution images which cannot identify the detailed structure of the neurons. We have developed a digital atlas methodology that combine…
Longitudinal fetal brain atlas is a powerful tool for understanding and characterizing the complex process of fetus brain development. Existing fetus brain atlases are typically constructed by averaged brain images on discrete time points independently over time. Due to the differences in onto-genetic trends among samples at different time points, the resulting atlases suffer from temporal inconsistency, which may lead to estimating error of the brain developmental characteristic parameters along the timeline. To this end, we proposed a multi-stage deep-learning framework to tackle the time in…
ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）
Functional connectivity network (FCN) data from functional magnetic resonance imaging (fMRI) is increasingly used for the diagnoses of brain disorders. However, state-of-the-art studies used to build the FCN using a single brain parcellation atlas at a certain spatial scale, which largely neglected functional interactions across different spatial scales in hierarchical manners. In this study, we propose a novel framework to perform multiscale FCN analysis for brain disorder diagnosis. We first use a set of well-defined multiscale atlases to compute multiscale FCNs. Then, we utilize biologicall…
Toward a Multi-View Brain Network Foundation Model: Cross-View Consistency Learning Across Arbitrary Atlases
Saw Swee Hock School of Public Health at National University of Singapore（国立新加坡大学 Saw Swee Hock 公共卫生学院）；School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University（上海科技大学 生物医学工程学院及先进医学材料与设备国家重点实验室）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学 计算与数据科学学院）；School of Automation Science and Electrical Engineering, Beihang University（北航自动化科学与电气工程学院）；State Key Laboratory of Virtual Reality Technology and Systems, Beihang University（北航虚拟现实技术与系统国家重点实验室）；Shanghai United Imaging Intelligence（上海联合影像智能有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）
The 4D Human Embryonic Brain Atlas: spatiotemporal atlas generation for rapid anatomical changes
Erasmus MC（埃拉斯姆斯医学院）；University Medical Center Groningen（格罗宁根大学医学中心）；Amsterdam UMC location University of Amsterdam（阿姆斯特丹大学阿姆斯特丹医学中心分部）；Amsterdam Reproduction and Development research institute（阿姆斯特丹生殖与发育研究所）
CEA, Fundamental Research Division (DRF), Institute Jacob, Molecular Imaging Research Center (MIRCen)（法国原子能与可替代能源委员会（CEA）基础研究部雅各布研究所分子影像研究中心（MIRCen））；WITSEE（WITSEE 公司）
A significant challenge for brain histological data analysis is to precisely identify anatomical regions in order to perform accurate local quantifications and evaluate therapeutic solutions. Usually, this task is performed manually, becoming therefore tedious and subjective. Another option is to use automatic or semi-automatic methods, among which segmentation using digital atlases co-registration. However, most available atlases are 3D, whereas digitized histological data are 2D. Methods to perform such 2D-3D segmentation from an atlas are required. This paper proposes a strategy to automati…
Precise identification of mouse brain microscopy images is a crucial first step when anatomical structures in the mouse brain are to be registered to a reference atlas. Practitioners usually rely on manual comparison of images or tools that assume the presence of complete images. This work explores Siamese Networks as the method for finding corresponding 2D reference atlas plates for given partial 2D mouse brain images. Siamese networks are a class of convolutional neural networks (CNNs) that use weight-shared paths to obtain low dimensional embeddings of pairs of input images. The corresponde…
Brain Research Institute (HiFo), UZH（苏黎世大学脑研究所（HiFo））；Neuroscience Center Zurich (ZNZ), UZH/ETH Zurich（苏黎世神经科学中心（ZNZ），苏黎世大学/苏黎世联邦理工学院）；Department of Biology (D-BIOL), ETH Zurich（苏黎世联邦理工学院生物系（D-BIOL））
Neuroscientists have devoted significant effort into the creation of standard brain reference atlases for high-throughput registration of anatomical regions of interest. However, variability in brain size and form across individuals poses a significant challenge for such reference atlases. To overcome these limitations, we introduce a fully automated deep neural network-based method (SeBRe) for registration through Segmenting Brain Regions of interest with minimal human supervision. We demonstrate the validity of our method on brain images from different mouse developmental time points, across…
Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusion
College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）；Saw Swee Hock School of Public Health at National University of Singapore（新加坡国立大学 Saw Swee Hock 公共卫生学院）；Cognitive Neuroimaging Centre and Lee Kong Chian School of Medicine, Nanyang Technological University（认知神经影像中心和 Lee Kong Chian 医学院，南洋理工大学）
The CLARITY method renders brains optically transparent to enable high-resolution imaging in the structurally intact brain. Anatomically annotating CLARITY brains is necessary for discovering which regions contain signals of interest. Manually annotating whole-brain, terabyte CLARITY images is difficult, time-consuming, subjective, and error-prone. Automatically registering CLARITY images to a pre-annotated brain atlas offers a solution, but is difficult for several reasons. Removal of the brain from the skull and subsequent storage and processing cause variable non-rigid deformations, thus co…
Imperial College London（伦敦帝国学院）；King’s College London（伦敦国王学院）；FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；Technical University of Munich（慕尼黑工业大学）
Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development. Accurate segmentation of the different brain tissues is a vital step in several brain analysis tasks, such as cortical surface reconstruction and tissue thickness measurements. Fetal MRI scans, however, are prone to motion artifacts that can affect the correctness of both manual and automatic segmentation techniques. In this paper, we propose a novel network structure that can simultaneously generate conditional atlases and predict brain tissue segmentation, called CAS-Net. The condition…
RIKEN Center for Brain Science（理化学研究所脑科学中心）；Institute of Fundamental Technological Research, Polish Academy of Sciences（波兰科学院基础技术研究所）；Univ Montpellier（蒙彼利埃大学）；IMT Mines Alès（法国国立高等矿业电信学校阿莱斯分校）
We present the first automated pipeline to create an atlas of in situ hybridization gene expression in the adult marmoset brain in the same stereotaxic space. The pipeline consists of segmentation of gene expression from microscopy images and registration of images to a standard space. Automation of this pipeline is necessary to analyze the large volume of data in the genome-wide whole-brain dataset, and to process images that have varying intensity profiles and expression patterns with minimal human bias. To reduce the number of labelled images required for training, we develop a semi-supervi…
White Matter Hyperintensities (WMH) are the most common manifestation of cerebral small vessel disease (cSVD) on the brain MRI. Accurate WMH segmentation algorithms are important to determine cSVD burden and its clinical consequences. Most of existing WMH segmentation algorithms require both fluid attenuated inversion recovery (FLAIR) images and T1-weighted images as inputs. However, T1-weighted images are typically not part of standard clinicalscans which are acquired for patients with acute stroke. In this paper, we propose a novel brain atlas guided attention U-Net (BAGAU-Net) that leverage…
Whole brain segmentation and cortical surface parcellation are essential in understanding the anatomical-functional relationships of the brain. Multi-atlas segmentation has been regarded as one of the leading segmentation methods for the whole brain segmentation. In our recent work, the multi-atlas technique has been adapted to surface reconstruction using a method called Multi-atlas CRUISE (MaCRUISE). The MaCRUISE method not only performed consistent volume-surface analyses but also showed advantages on robustness compared with the FreeSurfer method. However, a detailed surface parcellation w…
Conditional Fetal Brain Atlas Learning for Automatic Tissue Segmentation
Computational Imaging Research Lab（计算成像研究实验室）；Department of Biomedical Imaging and Image-guided Therapy（生物医学成像与影像引导治疗部门）；Medical University of Vienna（维也纳医科大学）；Comprehensive Center for Artificial Intelligence in Medicine（医学人工智能综合中心）；Division of General Radiology（普通放射学分会）；Medical University Vienna（维也纳医科大学）；Christian Doppler Laboratory for Mathematical Modelling and Simulation of Next-Generation Medical Ultrasound Devices（基督教·多普勒医学影像建模与模拟实验室）
University College London（伦敦大学学院）；University Hospital Zurich（苏黎世大学医院）；University of Zurich（苏黎世大学）
In this paper we will focus on the potential and on the challenges associated with the development of an integrated brain and spinal cord modelling framework for processing MR neuroimaging data. The aim of the work is to explore how a hierarchical generative model of imaging data, which captures simultaneously the distribution of signal intensities and the variability of anatomical shapes across a large population of subjects, can serve to quantitatively investigate, in vivo, the morphology of the central nervous system (CNS). In fact, the generality of the proposed Bayesian approach, which ex…
UCSD（加州大学圣地亚哥分校）；Salk Institute for Biological Studies（索尔克生物研究所）
Brightfield and fluorescent imaging of whole brain sections are funda- mental tools of research in mouse brain study. As sectioning and imaging become more efficient, there is an increasing need to automate the post-processing of sec- tions for alignment and three dimensional visualization. There is a further need to facilitate the development of a digital atlas, i.e. a brain-wide map annotated with cell type and tract tracing data, which would allow the automatic registra- tion of images stacks to a common coordinate system. Currently, registration of slices requires manual identification of …
Brain segmentation based on multi-atlas guided 3D fully convolutional network ensembles
School of Electronics and Information Technology, Sun Yat-sen University（中山大学电子与信息工程学院）；Department of Electrical and Electronic Engineering, Southern University of Science and Technology（南方科技大学电子与电气工程学院）
We propose an unsupervised deep learning method for atlas based registration to achieve segmentation and spatial alignment of the embryonic brain in a single framework. Our approach consists of two sequential networks with a specifically designed loss function to address the challenges in 3D first trimester ultrasound. The first part learns the affine transformation and the second part learns the voxelwise nonrigid deformation between the target image and the atlas. We trained this network end-to-end and validated it against a ground truth on synthetic datasets designed to resemble the challen…
CINeMA: Conditional Implicit Neural Multi-Modal Atlas for a Spatio-Temporal Representation of the Perinatal Brain
School of Computation, Information and Technology, and the School of Medicine and Health（计算信息学院及医学健康学院）；Technical University of Munich（慕尼黑技术大学）；Department of Computing（计算系）；Institut de Neurosciences de la Timone, UMR 7289, CNRS, Aix-Marseille Université（神经科学研究所，UMR 7289，CNRS，艾克斯-马赛大学）；Aix-Marseille Univ, APHM, Service de Neuroradiologie Diagnostique et Interventionnelle, Hôpital de la Timone（艾克斯-马赛大学，APHM，诊断与介入神经放射科，泰米翁医院）；Aix-Marseille Univ, APHM, service de neurologie pédiatrique, Hôpital de la Timone（艾克斯-马赛大学，APHM，儿童神经科，泰米翁医院）
AtlasSeg: Atlas Prior Guided Dual-U-Net for Cortical Segmentation in Fetal Brain MRI
College of Biomedical Engineering & Instrument Science, Zhejiang University（浙江大学生物医学工程与仪器科学学院）；Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences（中国医学科学院北京医院国家老年医学中心老年医学研究所）；Shandong Provincial Hospital Affiliated to Shandong First Medical University（山东第一医科大学附属山东省立医院）；State Key Lab of CAD&CG, Zhejiang University（浙江大学CAD&CG国家重点实验室）
JOSA: Joint surface-based registration and atlas construction of brain geometry and function
McGovern Institute for Brain Research, MIT（麦戈文脑科学研究所（麻省理工学院））；Harvard University（哈佛大学）；Computer Science and Artificial Intelligence Laboratory, MIT（计算机科学与人工智能实验室（麻省理工学院））
An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis
BCN MedTech；Universitat Pompeu Fabra（庞培法布拉大学）；Hospital Clínic（临床医院）；Hospital Sant Joan de Déu（圣胡安·德杜医院）；University of Barcelona（巴塞罗那大学）；McConnell Brain Imaging Centre（麦康奈尔脑成像中心）；Montreal Neurological Institute and Hospital（蒙特利尔神经学研究所及医院）；McGill University（麦吉尔大学）；Centre for Biomedical Research on Rare Diseases (CIBERER)（罕见病生物医学研究中心（CIBERER））
SLIM-Brain: A Data- and Training-Efficient Foundation Model for fMRI Data Analysis
Department of Biomedical Engineering, Southern University of Science and Technology, China（南方科技大学生物医学工程系）；Department of Computer Science, University of Warwick, The UK（沃里克大学计算机科学系）；Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, China（复旦大学脑启发式智能科学技术研究所）
Silesian University in Opava（奥帕瓦西里西亚大学）；University of Oxford（牛津大学）；Ostrava University（俄斯特拉发大学）
Matching MRI brain images between patients or mapping patients' MRI slices to the simulated atlas of a brain is key to the automatic registration of MRI of a brain. The ability to match MRI images would also enable such applications as indexing and searching MRI images among multiple patients or selecting images from the region of interest. In this work, we have introduced robustness, accuracy and cumulative distance metrics and methodology that allows us to compare different techniques and approaches in matching brain MRI of different patients or matching MRI brain slice to a position in the …
The frequency-specific coupling mechanism of the functional human brain networks underpins its complex cognitive and behavioral functions. Nevertheless, it is not well unveiled what are the frequency-specific subdivisions and network topologies of the human brain. In this study, we estimated functional connectivity of the human cerebral cortex using spectral connection, and conducted frequency-specific parcellation using eigen-clustering and gradient-based methods, and then explored their topological structures. 7T fMRI data of 184 subjects in the HCP dataset were used for parcellation and exp…
Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València（瓦伦西亚理工大学先进信息技术与通信应用研究所 (ITACA)）；CNRS（法国国家科学研究中心）；Univ. Bordeaux（波尔多大学）；Bordeaux INP（波尔多国立高等工程技术学校）；Universitat de Valencia（瓦伦西亚大学）；University Valencia（瓦伦西亚大学）；EPHE（巴黎高等研究实践学院）；CHU de Bordeaux（波尔多大学医院中心）；Institut des Maladies Neurodégénératives（神经退行性疾病研究所）；CEA Saclay（法国原子能和替代能源委员会萨克雷）；Instituto de Automática e Informática Industrial, Universitat Politècnica de València（瓦伦西亚大学工业自动化与计算机研究所）；Hospital Universitario y Politécnico La Fe（拉菲大学综合医院）；Fundación para el Fomento de la Investigación Sanitario y Biomédica de la Comunidad Valenciana（瓦伦西亚社区健康与生物医学研究促进基金会）
University College London（伦敦大学学院）；Basque Center on Cognition, Brain and Language（巴斯克认知、大脑与语言中心）；University of Castilla-La Mancha（卡斯蒂利亚-拉曼恰大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Technical University of Denmark（丹麦技术大学）
The human thalamus is a brain structure that comprises numerous, highly specific nuclei. Since these nuclei are known to have different functions and to be connected to different areas of the cerebral cortex, it is of great interest for the neuroimaging community to study their volume, shape and connectivity in vivo with MRI. In this study, we present a probabilistic atlas of the thalamic nuclei built using ex vivo brain MRI scans and histological data, as well as the application of the atlas to in vivo MRI segmentation. The atlas was built using manual delineation of 26 thalamic nuclei on the…
The Mind Research Network（心智研究网络）；University of New Mexico（新墨西哥大学）
Large scale studies of group differences in healthy controls and patients and screenings for early stage disease prevention programs require processing and analysis of extensive multisubject datasets. Complexity of the task increases even further when segmenting structural MRI of the brain into an atlas with more than 50 regions. Current automatic approaches are time-consuming and hardly scalable; they often involve many error prone intermediate steps and don't utilize other available modalities. To alleviate these problems, we propose a feedforward fully convolutional neural network trained o…
UNC Chapel Hill（北卡罗来纳大学教堂山分校）；University of Pennsylvania（宾夕法尼亚大学）
Registration of images with pathologies is challenging due to tissue appearance changes and missing correspondences caused by the pathologies. Moreover, mass effects as observed for brain tumors may displace tissue, creating larger deformations over time than what is observed in a healthy brain. Deep learning models have successfully been applied to image registration to offer dramatic speed up and to use surrogate information (e.g., segmentations) during training. However, existing approaches focus on learning registration models using images from healthy patients. They are therefore not desi…
Comparative Study of Probabilistic Atlas and Deep Learning Approaches for Automatic Brain Tissue Segmentation from MRI Using N4 Bias Field Correction and Anisotropic Diffusion Pre-processing Techniques
University of Girona（赫罗纳大学）；University of Burgundy（勃艮第大学）；University of Cassino and Southern Lazio（卡西诺和南拉齐奥大学）
University College London（伦敦大学学院）；Inria（法国国家信息与自动化研究所）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Massachusetts Institute of Technology（麻省理工学院）
Joint registration of a stack of 2D histological sections to recover 3D structure (``3D histology reconstruction'') finds application in areas such as atlas building and validation of \emph{in vivo} imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as ``banana effect'' (straightening of curved structures) and ``z-shift'' (drift). While these problems can be alleviated with an external, linearly aligned reference (e.g., Magnetic Resonance (MR) images), registration is often inaccurate due to contrast difference…
Deep-Learning Atlas Registration for Melanoma Brain Metastases: Preserving Pathology While Enabling Cohort-Level Analyses
Department of Radiation Oncology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg（放射肿瘤科，弗赖堡大学医学中心-弗赖堡大学医学院）；Division of Medical Physics, Department of Radiation Oncology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg（医学物理系，放射肿瘤科，弗赖堡大学医学中心-弗赖堡大学医学院）；Department of Radiation Oncology, University Medical Center Schleswig-Holstein（放射肿瘤科，石勒苏益格-荷尔斯泰因大学医学中心）；Department of Radiation Oncology, TUM School of Medicine and Health, TUM University Hospital Rechts der Isar, Technical University of Munich（放射肿瘤科，慕尼黑技术大学医学院及健康科学系，慕尼黑技术大学医院雷chts der Isar）
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Atlas-based methods are the standard approaches for automatic targeting of the Anterior Nucleus of the Thalamus (ANT) for Deep Brain Stimulation (DBS), but these are known to lack robustness when anatomic differences between atlases and subjects are large. To improve the localization robustness, we propose a novel two-stage deep learning (DL) framework, where the first stage identifies and crops the thalamus regions from the whole brain MRI and the second stage performs per-voxel regression on the cropped volume to localize the targets at the finest resolution scale. To address the issue of da…
Shanghai United Imaging Intelligence Co. Ltd（上海联影智能医疗科技有限公司）；Southern Medical University（南方医科大学）
In brain tumor diagnosis and surgical planning, segmentation of tumor regions and accurate analysis of surrounding normal tissues are necessary for physicians. Pathological variability often renders difficulty to register a well-labeled normal atlas to such images and to automatic segment/label surrounding normal brain tissues. In this paper, we propose a new registration approach that first segments brain tumor using a U-Net and then simulates missed normal tissues within the tumor region using a partial convolutional network. Then, a standard normal brain atlas image is registered onto such …
One-shot segmentation of brain tissues is typically a dual-model iterative learning: a registration model (reg-model) warps a carefully-labeled atlas onto unlabeled images to initialize their pseudo masks for training a segmentation model (seg-model); the seg-model revises the pseudo masks to enhance the reg-model for a better warping in the next iteration. However, there is a key weakness in such dual-model iteration that the spatial misalignment inevitably caused by the reg-model could misguide the seg-model, which makes it converge on an inferior segmentation performance eventually. In this…
Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）
Segmentation of brain magnetic resonance images (MRI) is crucial for the analysis of the human brain and diagnosis of various brain disorders. The drawbacks of time-consuming and error-prone manual delineation procedures are aimed to be alleviated by atlas-based and supervised machine learning methods where the former methods are computationally intense and the latter methods lack a sufficiently large number of labeled data. With this motivation, we propose CORPS, a semi-supervised segmentation framework built upon a novel atlas-based pseudo-labeling method and a 3D deep convolutional neural n…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Whole brain segmentation on structural magnetic resonance imaging (MRI) is essential for understanding neuroanatomical-functional relationships. Traditionally, multi-atlas segmentation has been regarded as the standard method for whole brain segmentation. In past few years, deep convolutional neural network (DCNN) segmentation methods have demonstrated their advantages in both accuracy and computational efficiency. Recently, we proposed the spatially localized atlas network tiles (SLANT) method, which is able to segment a 3D MRI brain scan into 132 anatomical regions. Commonly, DCNN segmentati…
Reference-Free 3D Reconstruction of Brain Dissection Slabs via Learned Atlas Coordinates
Martinos Center for Biomedical Imaging (Martinos Center) at Massachusetts General Hospital (MGH) & Harvard Medical School (HMS)（马萨诸塞州总医院（MGH）及哈佛医学院（HMS）的生物医学成像中心（Martinos Center））；Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT)（麻省理工学院（MIT）的计算机科学与人工智能实验室（CSAIL））；Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, Copenhagen, Denmark（丹麦磁共振研究中心、功能与诊断成像及研究中心，哥本哈根大学医院-阿迈厄斯和赫维多尔，哥本哈根，丹麦）；Massachusetts Alzheimer’s Disease Research Center at MGH & HMS（马萨诸塞州总医院（MGH）及哈佛医学院（HMS）的阿尔茨海默病研究中心）；University of Washington（华盛顿大学）；Pathology Department at MGH & HMS（马萨诸塞州总医院（MGH）及哈佛医学院（HMS）的病理部门）；Neurology Department at MGH & HMS（马萨诸塞州总医院（MGH）及哈佛医学院（HMS）的神经病学部门）；Department of（部门）
Unsupervised Detection of Post-Stroke Brain Abnormalities
Univ Rennes（里昂大学）；Inria（法国国家信息与自动化技术研究院）；CNRS（法国国家科学研究中心）；Inserm（法国国家医学研究院）；IRISA UMR 6074（里昂信息科学与自动化研究院）；CHU Rennes（里昂大学医院）；Siemens Healthineers（西门子医疗）；Centre de Kerpape（凯帕普中心）
Emory University（埃默里大学）；University of Wyoming（怀俄明大学）；Colgate University（科尔盖特大学）；University of Utah（犹他大学）
Two segmentation methods, one atlas-based and one neural-network-based, were compared to see how well they can each automatically segment the brain stem and cerebellum in Displacement Encoding with Stimulated Echoes Magnetic Resonance Imaging (DENSE-MRI) data. The segmentation is a pre-requisite for estimating the average displacements in these regions, which have recently been proposed as biomarkers in the diagnosis of Chiari Malformation type I (CMI). In numerical experiments, the segmentations of both methods were similar to manual segmentations provided by trained experts. It was found tha…
Unsupervised Segmentation of Fetal Brain MRI using Deep Learning Cascaded Registration
Universitat Pompeu Fabra（庞培法布拉大学）；BCNatal；Institución Catalana de Investigación y Estudios Avanzados (ICREA)（加泰罗尼亚高级研究与发展研究院）；Centre for Biomedical Research on Rare Diseases (CIBERER)（罕见疾病生物医学研究中心）；European Commission, Joint Research Centre (JRC)（欧盟委员会联合研究中心）
University of Brescia（布雷西亚大学）；University of Glasgow（格拉斯哥大学）
Many functional and structural neuroimaging studies call for accurate morphometric segmentation of different brain structures starting from image intensity values of MRI scans. Current automatic (multi-) atlas-based segmentation strategies often lack accuracy on difficult-to-segment brain structures and, since these methods rely on atlas-to-scan alignment, they may take long processing times. Recently, methods deploying solutions based on Convolutional Neural Networks (CNNs) are making the direct analysis of out-of-the-scanner data feasible. However, current CNN-based solutions partition the t…
University of Alberta（阿尔伯塔大学）；The Hospital for Sick Children（病童医院）；University of Toronto（多伦多大学）
White Matter Injury (WMI) is the most prevalent brain injury in the preterm neonate leading to developmental deficits. However, detecting WMI in Magnetic Resonance (MR) images of preterm neonate brains using traditional WM segmentation-based methods is difficult mainly due to lack of reliable preterm neonate brain atlases to guide segmentation. Hence, we propose a segmentation-free, fast, unsupervised, atlas-free WMI detection method. We detect the ventricles as blobs using a fast linear Maximally Stable Extremal Regions algorithm. A reference contour equidistant from the blobs and the brain-b…
University of Oxford（牛津大学）；Leiden University Medical Center（莱顿大学医学中心）；Shanghai Jiao Tong University（上海交通大学）
Two-dimensional (2D) freehand ultrasound is the mainstay in prenatal care and fetal growth monitoring. The task of matching corresponding cross-sectional planes in the 3D anatomy for a given 2D ultrasound brain scan is essential in freehand scanning, but challenging. We propose AdLocUI, a framework that Adaptively Localizes 2D Ultrasound Images in the 3D anatomical atlas without using any external tracking sensor.. We first train a convolutional neural network with 2D slices sampled from co-aligned 3D ultrasound volumes to predict their locations in the 3D anatomical atlas. Next, we fine-tune …
In this work, we improve the performance of multi-atlas segmentation (MAS) by integrating the recently proposed VoteNet model with the joint label fusion (JLF) approach. Specifically, we first illustrate that using a deep convolutional neural network to predict atlas probabilities can better distinguish correct atlas labels from incorrect ones than relying on image intensity difference as is typical in JLF. Motivated by this finding, we propose VoteNet+, an improved deep network to locally predict the probability of an atlas label to differs from the label of the target image. Furthermore, we …
StyleSeg V2: Towards Robust One-shot Segmentation of Brain Tissue via Optimization-free Registration Error Perception
Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology（华中科技大学武汉光电国家研究中心）；Wuhan United Imaging Healthcare Surgical Technology Co., Ltd.（武汉联影外科技术有限公司）
School of Computing, Informatics and Decision Systems Engineering, Arizona State University（亚利桑那州立大学计算、信息与决策系统工程学院）
A large number of surface-based analyses on brain imaging data adopt some specific brain atlases to better assess structural and functional changes in one or more brain regions. In these analyses, it is necessary to obtain an anatomically correct surface parcellation scheme in an individual brain by referring to the given atlas. Traditional ways to accomplish this goal are through a designed surface-based registration or hand-crafted surface features, although both of them are time-consuming. A recent deep learning approach depends on a regular spherical parameterization of the mesh, which is …
