Next Article in Journal
A Collection of 13 Archaeal and 46 Bacterial Genomes Reconstructed from Marine Metagenomes Derived from the North Sea
Next Article in Special Issue
Bioinformatics Analysis Identifying Key Biomarkers in Bladder Cancer
Previous Article in Journal / Special Issue
The Fundamental Clustering and Projection Suite (FCPS): A Dataset Collection to Test the Performance of Clustering and Data Projection Algorithms
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Data
Volume 5
Issue 1
10.3390/data5010014
data-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Data Descriptor

Intracranial Hemorrhage Segmentation Using a Deep Convolutional Model

by
Murtadha D. Hssayeni
1,2,
Muayad S. Croock
2,
Aymen D. Salman
2,
Hassan Falah Al-khafaji
3,
Zakaria A. Yahya
3 and
Behnaz Ghoraani
1,*
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.
Data 2020, 5(1), 14; https://doi.org/10.3390/data5010014
Submission 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)
Browse Figures
Versions Notes

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 intracranial hemorrhage segmentation; ICH detection; fully convolutional network; U-Net; CT scans dataset

Share and Cite

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. https://doi.org/10.3390/data5010014

AMA Style

Hssayeni MD, Croock MS, Salman AD, Al-khafaji HF, Yahya ZA, Ghoraani B. Intracranial Hemorrhage Segmentation Using a Deep Convolutional Model. Data. 2020; 5(1):14. https://doi.org/10.3390/data5010014

Chicago/Turabian Style

Hssayeni, Murtadha D., Muayad S. Croock, Aymen D. Salman, Hassan Falah Al-khafaji, Zakaria A. Yahya, and Behnaz Ghoraani. 2020. "Intracranial Hemorrhage Segmentation Using a Deep Convolutional Model" Data 5, no. 1: 14. https://doi.org/10.3390/data5010014

APA Style

Hssayeni, M. D., Croock, M. S., Salman, A. D., Al-khafaji, H. F., Yahya, Z. A., & Ghoraani, B. (2020). Intracranial Hemorrhage Segmentation Using a Deep Convolutional Model. Data, 5(1), 14. https://doi.org/10.3390/data5010014

Article Metrics

Back to TopTop