Segmentation and approximation of blood volume in intracranial hemorrhage patients based on computed tomography scan images using deep learning method

Kezia Irene, M. Anwar Masum, Reyhan Eddy Yunus, Wisnu Jatmiko

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Traumatic brain injury is a common injury that can range from mild concussions to severe permanent brain damage. One of the severe damages caused by traumatic brain injury is intracranial hemorrhage, which is typically diagnosed by clinicians using head computed tomography (CT) scans. However, in some hospitals in Indonesia, sometimes there is a lack of clinicians who are able to interpret the CT scan results, leading to morbidity and mortality. Deep learning algorithms, especially convolutional neural networks (CNN) can be utilized to help clinicians in diagnosing patients with intracranial hemorrhage. In this study, we propose an automated segmentation and blood volume approximation of intracranial hemorrhage patients from CT scan images using deep learning and regression methods. For the blood segmentation, we utilized Dynamic Graph Convolutional Neural Network (DGCNN) architecture and for the blood volume approximation, we utilized regression methods. The dataset for this work consists of 27 head CT scans obtained from the Cipto Mangunkusumo National General Hospital 2019 traumatic brain injury data segmented manually by a radiologist. For blood segmentation, we proposed several scenarios by upsampling or downsampling the data. The best results obtained in the scenario without doing upsampling resulted in a sensitivity of 97.8% and a specificity of 95.6%. For blood volume approximation, the best results are obtained using the support vector machine (SVM) method with a radial basis function (RBF) kernel, with a mean squared error of \mathbf{3.67x10}{\wedge}\mathbf{4}.

Original languageEnglish
Title of host publication2020 International Workshop on Big Data and Information Security, IWBIS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages65-72
Number of pages8
ISBN (Electronic)9781728190983
DOIs
Publication statusPublished - 17 Oct 2020
Event5th International Workshop on Big Data and Information Security, IWBIS 2020 - Depok, Indonesia
Duration: 17 Oct 202018 Oct 2020

Publication series

Name2020 International Workshop on Big Data and Information Security, IWBIS 2020

Conference

Conference5th International Workshop on Big Data and Information Security, IWBIS 2020
Country/TerritoryIndonesia
CityDepok
Period17/10/2018/10/20

Keywords

  • blood volume approximation
  • CT scan
  • DGCNN
  • Intracranial hemorrhage
  • three-dimensional CNN

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