Intracranial Hemorrhage Segmentation Using a Deep Convolutional Model

Murtadha D. Hssayeni, Muayad S. Croock, Aymen D. Salman, Hassan Falah Al-khafaji, Zakaria A. Yahya and Behnaz Ghoraani
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Murtadha D. Hssayeni: The Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
Muayad S. Croock: Computer Engineering Department, University of Technology, Baghdad 10001, Iraq
Aymen D. Salman: Computer Engineering Department, University of Technology, Baghdad 10001, Iraq
Hassan Falah Al-khafaji: Babylon Health Directorate, Babil 51001, Iraq
Zakaria A. Yahya: Babylon Health Directorate, Babil 51001, Iraq
Behnaz Ghoraani: The Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA

Data, 2020, vol. 5, issue 1, 1-18

Abstract: 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.

Keywords: intracranial hemorrhage segmentation; ICH detection; fully convolutional network; U-Net; CT scans dataset (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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