BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset
Australia Institute of Machine Learning, University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；Australian National University（澳大利亚国立大学）；Flinders Health and Medical Research Institute, Flinders University（弗林德斯大学弗林德斯健康与医学研究所）；ETH Zürich（苏黎世联邦理工学院）
Intracranial hemorrhage (ICH) is a pathological condition characterized by bleeding inside the skull or brain, which can be attributed to various factors. Identifying, localizing and quantifying ICH has important clinical implications, in a bleed-dependent manner. While deep learning techniques are widely used in medical image segmentation and have been applied to the ICH segmentation task, existing public ICH datasets do not support the multi-class segmentation problem. To address this, we develop the Brain Hemorrhage Segmentation Dataset (BHSD), which provides a 3D multi-class ICH dataset containing 192 volumes with pixel-level annotations and 2200 volumes with slice-level annotations across five categories of ICH. To demonstrate the utility of the dataset, we formulate a series of supervised and semi-supervised ICH segmentation tasks. We provide experimental results with state-of-the-art models as reference benchmarks for further model developments and evaluations on this dataset.
Benchmarking Class Activation Map Methods for Explainable Brain Hemorrhage Classification on Hemorica Dataset
Faculty of Computer Engineering, K. N. Toosi University of Technology（计算机工程学院，K.N.托菲大学）；Faculty of Electrical Engineering, K. N. Toosi University of Technology（电气工程学院，K.N.托菲大学）；Faculty of Mechanical Engineering, Tarbiat Modares University（机械工程学院，塔里比亚特莫达res大学）
SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentation
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Research Center for Medical Artificial Intelligence, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院医学人工智能研究中心）；Beijing Tiantan Hospital, Capital Medical University（首都医科大学北京天坛医院）
Queen Elizabeth Hospital（伊丽莎白女王医院）；The Chinese University of Hong Kong（香港中文大学）
Brain midline shift (MLS) is one of the most critical factors to be considered for clinical diagnosis and treatment decision-making for intracranial hemorrhage. Existing computational methods on MLS quantification not only require intensive labeling in millimeter-level measurement but also suffer from poor performance due to their dependence on specific landmarks or simplified anatomical assumptions. In this paper, we propose a novel semi-supervised framework to accurately measure the scale of MLS from head CT scans. We formulate the MLS measurement task as a deformation estimation problem and…
Dual-Task Vision Transformer for Rapid and Accurate Intracerebral Hemorrhage CT Image Classification
Franklin College of Arts and Sciences, University of Georgia（佐治亚大学富兰克林文理学院）；The First Affiliated Hospital of Xi’an Jiaotong University（西安交通大学第一附属医院）；The First Hospital of Yulin（榆林市第一医院）；Lanzhou University（兰州大学）；College of Agricultural & Environmental Sciences, University of Georgia（佐治亚大学农业与环境科学学院）；College of Engineering, University of Georgia（佐治亚大学工程学院）
PHE-SICH-CT-IDS: A Benchmark CT Image Dataset for Evaluation Semantic Segmentation, Object Detection and Radiomic Feature Extraction of Perihematomal Edema in Spontaneous Intracerebral Hemorrhage
Northeastern University（东北大学）；Shengjing Hospital, China Medical University（中国医科大学附属盛京医院）；University of Luebeck（吕贝克大学）
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
Intracranial hemorrhage occurs when blood vessels rupture or leak within the brain tissue or elsewhere inside the skull. It can be caused by physical trauma or by various medical conditions and in many cases leads to death. The treatment must be started as soon as possible, and therefore the hemorrhage should be diagnosed accurately and quickly. The diagnosis is usually performed by a radiologist who analyses a Computed Tomography (CT) scan containing a large number of cross-sectional images throughout the brain. Analysing each image manually can be very time-consuming, but automated technique…
We describe a deep learning approach for automated brain hemorrhage detection from computed tomography (CT) scans. Our model emulates the procedure followed by radiologists to analyse a 3D CT scan in real-world. Similar to radiologists, the model sifts through 2D cross-sectional slices while paying close attention to potential hemorrhagic regions. Further, the model utilizes 3D context from neighboring slices to improve predictions at each slice and subsequently, aggregates the slice-level predictions to provide diagnosis at CT level. We refer to our proposed approach as Recurrent Attention De…
Florida Atlantic University（佛罗里达大西洋大学）；University of Technology（理工大学）；Babylon Health Directorate（巴比伦卫生局）
Traumatic brain injuries could cause intracranial hemorrhage (ICH). ICH could lead to disability or death if it is not accurately diagnosed and treated in a time-sensitive procedure. The current clinical protocol to diagnose ICH is examining Computerized Tomography (CT) scans by radiologists to detect ICH and localize its regions. However, this process relies heavily on the availability of an experienced radiologist. In this paper, we designed a study protocol to collect a dataset of 82 CT scans of subjects with traumatic brain injury. Later, the ICH regions were manually delineated in each sl…
GCS-ICHNet: Assessment of Intracerebral Hemorrhage Prognosis using Self-Attention with Domain Knowledge Integration
Hangzhou Dianzi University（杭州电子科技大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Ping An Technology（平安科技）；Longgang District Central Hospital of Shenzhen（深圳市龙岗区中心医院）；National Health Data Institute, Shenzhen（深圳国家健康数据研究院）
Multi-Plane Vision Transformer for Hemorrhage Classification Using Axial and Sagittal MRI Data
Digital Technology and Innovation, Siemens Healthineers（西门子医疗数字技术与创新部）；Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg（弗赖堡-艾尔朗根-纽伦堡大学计算机科学系模式识别实验室）
Annotation-free deep learning for detection and segmentation of fetal germinal matrix-intraventricular hemorrhage in brain MRI
Department of Radiology, West China Second University Hospital, Sichuan University（四川大学华西第二医院放射科）；School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University（清华大学医学院生物医学工程系）；Department of Radiology, Sichuan Provincial Woman’s and Children’s Hospital, The Affiliated Women’s and Children’s Hospital of Chengdu Medical College（四川省妇幼保健院放射科，成都医学院附属妇幼医院）；Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China（成都妇女儿童中央医院，电子科技大学医学院）；Department of Radiology, The Third Affiliated Hospital of Zhengzhou University（郑州大学第三附属医院放射科）；Qujing Maternal and Child Health Hospital, Qujing, China（曲靖 maternal and child health hospital, Qujing, China）
Warren Alpert Medical School of Brown University（布朗大学沃伦·阿尔珀特医学院）；Emory University School of Medicine（埃默里大学医学院）；University of Florida School of Medicine（佛罗里达大学医学院）；Brown University（布朗大学）
Automated segmentation of medical imaging is of broad interest to clinicians and machine learning researchers alike. The goal of segmentation is to increase efficiency and simplicity of visualization and quantification of regions of interest within a medical image. Image segmentation is a difficult task because of multiparametric heterogeneity within the images, an obstacle that has proven especially challenging in efforts to automate the segmentation of brain lesions from non-contrast head computed tomography (CT). In this research, we have experimented with multiple available deep learning a…
Vision Transformer for Intracranial Hemorrhage Classification in CT Scans Using an Entropy-Aware Fuzzy Integral Strategy for Adaptive Scan-Level Decision Fusion
School of Electrical and Computer Engineering, College of Engineering, University of Tehran（塔里斯坦大学电气与计算机工程学院）；Department of Computer Science and Engineering, Sejong University（世宗大学计算机科学与工程系）
AI-Powered Intracranial Hemorrhage Detection: A Co-Scale Convolutional Attention Model with Uncertainty-Based Fuzzy Integral Operator and Feature Screening
School of Electrical and Computer Engineering, College of Engineering, University of Tehran（塔里斯坦大学电气与计算机工程学院）；Department of Computer Science and Engineering, Sejong University（世宗大学计算机科学与工程系）；Department of Neurosurgery, Iran University of Medical Sciences（伊朗医学科学大学神经外科系）
Whiting School of Engineering, Johns Hopkins University（约翰斯·霍普金斯大学怀廷工程学院）；Rutgers, The State University of New Jersey（罗格斯新泽西州立大学）
Automatic segmentation of anatomical landmarks from ultrasound (US) plays an important role in the management of preterm neonates with a very low birth weight due to the increased risk of developing intraventricular hemorrhage (IVH) or other complications. One major problem in developing an automatic segmentation method for this task is the limited availability of annotated data. To tackle this issue, we propose a novel image synthesis method using multi-scale self attention generator to synthesize US images from various segmentation masks. We show that our method can synthesize high-quality U…
Dual Attention Residual U-Net for Accurate Brain Ultrasound Segmentation in IVH Detection
Chongqing Electric Power College（重庆电力学院）；Chongqing Metropolitan College of Science and Technology（重庆市级科学研究院）；Xinqiao Hospital, Army Medical University（新桥医院，军队医学大学）
Intracranial hemorrhages (ICHs) are life-threatening brain injures with a relatively high incidence. In this paper, the automatic algorithm for the detection and classification of ICHs, including localization, is present. The set of binary convolutional neural network-based classifiers with a designed cascade-parallel architecture is used. This automatic system may lead to a distinct decrease in the diagnostic process's duration in acute cases. An average Jaccard coefficient of 53.7 % is achieved on the data from the publicly available head CT dataset CQ500.
