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The Fundamental Clustering and Projection Suite (FCPS): A Dataset Collection to Test the Performance of Clustering and Data Projection Algorithms
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Intracranial Hemorrhage Segmentation Using A Deep Convolutional Model

1
The Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
2
Computer Engineering Department, University of Technology, Baghdad 10001, Iraq
3
Babylon Health Directorate, Babil 51001, Iraq
*
Author to whom correspondence should be addressed.
Received: 15 November 2019 / Revised: 27 January 2020 / Accepted: 29 January 2020 / Published: 1 February 2020
(This article belongs to the Special Issue Benchmarking Datasets in Bioinformatics)
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 intracranial hemorrhage segmentation; ICH detection; fully convolutional network; U-Net; CT scans dataset
MDPI and ACS Style

Hssayeni, M.D.; Croock, M.S.; Salman, A.D.; Al-khafaji, H.F.; Yahya, Z.A.; Ghoraani, B. Intracranial Hemorrhage Segmentation Using A Deep Convolutional Model. Data 2020, 5, 14.

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