Novel solutions toward high accuracy automatic brain tissue classification in young children
Nataliya Portman, Paule-J Toussaint, Alan C. Evans McConnell
备注：
This manuscript (25 pages, 16 figures) was written in the end of N.Portman's postdoctoral assignment at Montreal Neurological Institute (MNI) in 2014. N. Portman's affiliation with MNI was valid from May 2010 until November 2013
Accurate automatic classification of major tissue classes and the cerebrospinal fluid in pediatric MR scans of early childhood brains remains a challenge. A poor and highly variable grey matter and white matter contrast on T1-weighted MR scans of developing brains complicates the automatic categorization of voxels into major tissue classes using state-of-the-art classification methods (Partial Volume Estimation). Varying intensities across brain tissues and possible tissue artifacts further contribute to misclassification. In order to improve the accuracy of automatic detection of major tissue types and the cerebrospinal fluid in infant's brains within the age range from 10 days to 4.5 years, we propose a new classification method based on Kernel Fisher Discriminant Analysis (KFDA) for pattern recognition, combined with an objective structural similarity index (SSIM) for perceptual image quality assessment. The proposed method performs an optimal partitioning of the image domain into subdomains having different average intensity values and relatively homogeneous tissue intensity. In the KFDA-based framework, a complex non-linear structure of grey matter, white matter and cerebrospinal fluid intensity clusters in a 3D (T1W, T2W, PDW)-space is exploited to find an accurate classification. Based on Computer Vision hypothesis that the Human Visual System is an optimal structural information extractor, the SSIM finds a new role in the evaluation of the quality of classification. A comparison with the state-of-the-art Partial Volume Estimation method using the SSIM index demonstrates superior performance of the local KFDA-based algorithm in low contrast subdomains and more accurate detection of grey matter, white matter, and cerebrospinal fluid patterns in the brain volume.
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 …
Brains with complex distortion of cerebral anatomy present several challenges to automatic tissue segmentation methods of T1-weighted MR images. First, the very high variability in the morphology of the tissues can be incompatible with the prior knowledge embedded within the algorithms. Second, the availability of MR images of distorted brains is very scarce, so the methods in the literature have not addressed such cases so far. In this work, we present the first evaluation of state-of-the-art automatic tissue segmentation pipelines on T1-weighted images of brains with different severity of co…
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…
Automatic segmentation of brain MR images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is critical for tissue volumetric analysis and cortical surface reconstruction. Due to dramatic structural and appearance changes associated with developmental and aging processes, existing brain tissue segmentation methods are only viable for specific age groups. Consequently, methods developed for one age group may fail for another. In this paper, we make the first attempt to segment brain tissues across the entire human lifespan (0-100 years of age) using a unified deep learning…
Convolutional neural networks (CNNs) are usually used as a backbone to design methods in biomedical image segmentation. However, the limitation of receptive field and large number of parameters limit the performance of these methods. In this paper, we propose a graph neural network (GNN) based method named GNN-SEG for the segmentation of brain tissues. Different to conventional CNN based methods, GNN-SEG takes superpixels as basic processing units and uses GNNs to learn the structure of brain tissues. Besides, inspired by the interaction mechanism in biological vision systems, we propose two k…
Most segmentation methods in child brain MRI are supervised and are based on global intensity distributions of major brain structures. The successful implementation of a supervised approach depends on availability of an age-appropriate probabilistic brain atlas. For the study of early normal brain development, the construction of such a brain atlas remains a significant challenge. Moreover, using global intensity statistics leads to inaccurate detection of major brain tissue classes due to substantial intensity variations of MR signal within the constituent parts of early developing brain. In …
A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network
Children’s Hospital of Philadelphia（费城儿童医院）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；National Cancer Institute（美国国家癌症研究所）；Children's National Hospital（儿童国家医院）；Sheikh Zayed Institute for Pediatric Surgical Innovation（谢赫扎耶德儿科外科创新研究院）
mViSE: A Visual Search Engine for Analyzing Multiplex IHC Brain Tissue Images
Cullen College of Engineering, University of Houston（休斯顿大学塞勒恩工程学院）；The University of Texas McGovern Medical School（德克萨斯大学麦格芬医学学院）；National Institute of Neurological Disorders and Stroke（国家神经疾病与中风研究所）
Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting
University Hospital Basel（巴塞尔大学医院）；Helmholtz Center Munich（慕尼黑亥姆霍兹中心）；Helmholtz AI（亥姆霍兹人工智能）；Technical University of Munich（慕尼黑工业大学）；University of Basel（巴塞尔大学）；University of Zurich（苏黎世大学）；Klinikum rechts der Isar（伊斯河畔医院）；TranslaTUM - Central Institute for Translational Cancer Research（慕尼黑工业大学转化肿瘤学中央研究所（TranslaTUM））
University Medical Center Utrecht（乌得勒支大学医学中心）；Wilhelmina Children’s Hospital（威廉明娜儿童医院）；Utrecht University（乌得勒支大学）
MR images of fetuses allow clinicians to detect brain abnormalities in an early stage of development. The cornerstone of volumetric and morphologic analysis in fetal MRI is segmentation of the fetal brain into different tissue classes. Manual segmentation is cumbersome and time consuming, hence automatic segmentation could substantially simplify the procedure. However, automatic brain tissue segmentation in these scans is challenging owing to artifacts including intensity inhomogeneity, caused in particular by spontaneous fetal movements during the scan. Unlike methods that estimate the bias f…
Columbia University（哥伦比亚大学）；Mortimer B. Zuckerman Mind Brain Behavior Institute（Mortimer B. Zuckerman 心智与脑行为研究所）
Brain tissue segmentation has demonstrated great utility in quantifying MRI data through Voxel-Based Morphometry and highlighting subtle structural changes associated with various conditions within the brain. However, manual segmentation is highly labor-intensive, and automated approaches have struggled due to properties inherent to MRI acquisition, leaving a great need for an effective segmentation tool. Despite the recent success of deep convolutional neural networks (CNNs) for brain tissue segmentation, many such solutions do not generalize well to new datasets, which is critical for a reli…
Brain Tissues Segmentation on MR Perfusion Images Using CUSUM Filter for Boundary Pixels
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”（乌克兰国立技术大学“伊戈尔·西科尔斯基基辅理工学院”）；Taras Shevchenko National University of Kyiv（基辅塔拉斯·舍甫琴科国立大学）
Synthetic magnetic resonance images for domain adaptation: Application to fetal brain tissue segmentation
Lausanne University Hospital (CHUV)（洛桑大学医院（CHUV））；University of Lausanne (UNIL)（洛桑大学（UNIL））；CIBM Center for Biomedical Imaging（CIBM生物医学成像中心）；University Children’s Hospital Zurich（苏黎世大学儿童医院）；University of Zurich（苏黎世大学）；Neuroscience Center Zurich（苏黎世神经科学中心）
Brain tumor segmentation intends to delineate tumor tissues from healthy brain tissues. The tumor tissues include necrosis, peritumoral edema, and active tumor. In contrast, healthy brain tissues include white matter, gray matter, and cerebrospinal fluid. The MRI based brain tumor segmentation research is gaining popularity as; 1. It does not irradiate ionized radiation like X-ray or computed tomography imaging. 2. It produces detailed pictures of internal body structures. The MRI scans are input to deep learning-based approaches which are useful for automatic brain tumor segmentation. The fea…
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…
During the first years of life, the human brain undergoes dynamic spatially-heterogeneous changes, involving differentiation of neuronal types, dendritic arborization, axonal ingrowth, outgrowth and retraction, synaptogenesis, and myelination. To better quantify these changes, this article presents a method for probing tissue microarchitecture by characterizing water diffusion in a spectrum of length scales, factoring out the effects of intra-voxel orientation heterogeneity. Our method is based on the spherical means of the diffusion signal, computed over gradient directions for a fixed set of…
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）
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）
King’s College London（伦敦国王学院）；School of Biomedical Engineering & Imaging Sciences（生物医学工程与成像科学学院）；King’s College Hospital NHS Foundation Trust（国王学院医院国民健康服务基金会信托）；NVIDIA（英伟达）；Dementia Research Centre, UCL Institute of Neurology, UCL（伦敦大学学院痴呆研究中心、伦敦大学学院神经学研究所、伦敦大学学院）；Department of Medical Physics, UCL（伦敦大学学院医学物理系）；Department of Neuroradiology（神经放射科）；St. Thomas’ Hospital（圣托马斯医院）
Brain tissue segmentation from multimodal MRI is a key building block of many neuroimaging analysis pipelines. Established tissue segmentation approaches have, however, not been developed to cope with large anatomical changes resulting from pathology, such as white matter lesions or tumours, and often fail in these cases. In the meantime, with the advent of deep neural networks (DNNs), segmentation of brain lesions has matured significantly. However, few existing approaches allow for the joint segmentation of normal tissue and brain lesions. Developing a DNN for such a joint task is currently …
Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes
ShanghaiTech University（上海科技大学）；Fudan University（复旦大学）；Obstetrics and Gynecology Hospital of Fudan University（复旦大学附属妇产科医院）；Northwestern Polytechnical University（西北工业大学）；Shanghai United Imaging Intelligence company Ltd.（上海联影智能科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究中心）
Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）
Automatic fetal brain tissue segmentation can enhance the quantitative assessment of brain development at this critical stage. Deep learning methods represent the state of the art in medical image segmentation and have also achieved impressive results in brain segmentation. However, effective training of a deep learning model to perform this task requires a large number of training images to represent the rapid development of the transient fetal brain structures. On the other hand, manual multi-label segmentation of a large number of 3D images is prohibitive. To address this challenge, we segm…
Improving cross-domain brain tissue segmentation in fetal MRI with synthetic data
Lausanne University Hospital（洛桑大学医院）；University of Lausanne (UNIL)（洛桑大学）；CIBM Center for Biomedical Imaging（CIBM生物医学成像中心）；King’s College London（伦敦国王学院）；FAU Erlangen-Nuremberg（埃尔朗根-纽伦堡大学）
Brain tissue segmentation from multimodal MRI is a key building block of many neuroscience analysis pipelines. It could also play an important role in many clinical imaging scenarios. Established tissue segmentation approaches have however not been developed to cope with large anatomical changes resulting from pathology. The effect of the presence of brain lesions, for example, on their performance is thus currently uncontrolled and practically unpredictable. Contrastingly, with the advent of deep neural networks (DNNs), segmentation of brain lesions has matured significantly and is achieving …
As bone and air produce weak signals with conventional MR sequences, segmentation of these tissues particularly difficult in MRI. We propose to integrate patch-based anatomical signatures and an auto-context model into a machine learning framework to iteratively segment MRI into air, bone and soft tissue. The proposed semantic classification random forest (SCRF) method consists of a training stage and a segmentation stage. During training stage, patch-based anatomical features were extracted from registered MRI-CT training images, and the most informative features were identified to train a se…
Shanghai Jiao Tong University（上海交通大学）；University of California, Berkeley（加州大学伯克利分校）；ShanghaiTech University（上海科技大学）；Zhejiang University School of Medicine（浙江大学医学院）；The Second Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第二医院）；Rui Jin Hospital, Shanghai Jiao Tong University, School of Medicine（上海交通大学医学院附属瑞金医院）；Stanford University（斯坦福大学）；Lucile Packard Children’s Hospital（露西尔·帕卡德儿童医院）；Helen Wills Neuroscience Institute（海伦·威尔斯神经科学研究所）
Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from MRI gradient-echo phase signal and typically requires several processing steps. These steps involve phase unwrapping, brain volume extraction, background phase removal and solving an ill-posed inverse problem. The resulting susceptibility map is known to suffer from inaccuracy near the edges of the brain tissues, in part due to imperfect brain extraction, edge erosion of the brain tissue and the lack of phase measurement outside the brain. This inaccuracy has thus hindered the application of …
An Adversarial Approach to Register Extreme Resolution Tissue Cleared 3D Brain Images
School of Computer Science, Queensland University of Technology（昆士兰科技大学计算机科学学院）；School of Electrical Engineering and Robotics, Queensland University of Technology（昆士兰科技大学电气工程与机器人学院）；Centre for Data Science, Queensland University of Technology（昆士兰科技大学数据科学中心）
The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting
Helmholtz AI, Germany（亥姆霍兹人工智能研究所）；Technical University of Munich, Germany（慕尼黑工业大学）；Helmholtz Munich, Germany（亥姆霍兹慕尼黑中心）；University of Zurich, Switzerland（苏黎世大学）；Klinikum Rechts der Isar, Munich, Germany（慕尼黑右岸医院）；SETLabs Research GmbH, Healthcare Technologies, Munich, Germany（SETLabs研究有限公司）；Ludwig Maximilian University, Munich, Germany（路德维希马克西米利安慕尼黑大学）
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）
