文章基本信息

标题：Intracranial Hemorrhage Segmentation Using a Deep Convolutional Model
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作者：Murtadha D. Hssayeni ; Muayad S. Croock ; Aymen D. Salman 等
期刊名称：Data
印刷版ISSN：2306-5729
出版年度：2020
卷号：5
期号：1
页码：14-31
DOI：10.3390/data5010014
出版社：MDPI Publishing
摘要：Traumatic brain injuries may cause intracranial hemorrhages (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 a traumatic brain injury. Next, the ICH regions were manually delineated in each slice by a consensus decision of two radiologists. The dataset is publicly available online at the PhysioNet repository for future analysis and comparisons. In addition to publishing the dataset, which is the main purpose of this manuscript, we implemented a deep Fully Convolutional Networks (FCNs), known as U-Net, to segment the ICH regions from the CT scans in a fully-automated manner. The method as a proof of concept achieved a Dice coefficient of 0.31 for the ICH segmentation based on 5-fold cross-validation.
关键词：intracranial hemorrhage segmentation; ICH detection; fully convolutional network; U-Net; CT scans dataset intracranial hemorrhage segmentation ; ICH detection ; fully convolutional network ; U-Net ; CT scans dataset