CNN BASED HIERARCHICAL INTRACEREBRAL HEMATOMA REGION EXTRACTION METHOD IN HEAD THICK-SLICE CT IMAGES

Kazunori Oka, Daisuke Fujita, Yasunobu Nohara, Inoue Sozo, Koichi Arimura, Koji Iihara, Syoji Kobashi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Cerebrovascular disease is the fourth leading cause of death in Japan, with approximately 100,000 deaths in 2019. The intracerebral hematoma (ICH) is difficult and time-consuming to interpret even for specialists, so an automated method for extracting ICH regions from brain computed-tomography (CT) images is needed to reduce the burden on physicians and improve the speed and accuracy of diagnosis. Because the ICH shows high-absorption in CT images, most of conventional methods segment the high-absorption regions. However, the high-absorption region includes hemorrhagic region such as subarachnoid hemorrhage and intraventricular hemorrhage. These are not ICH. In this study, we propose an automatic extraction method for ICH regions from brain CT images, and the proposed method aims to reduce the over-extraction of high-absorption regions. The proposed method focuses on the anatomical structure of ICH, extracts the high-absorption regions, and proposes a hierarchical method based on classification using convolutional neural network (CNN). The model was trained and evaluated on 33 subjects, and over-extraction was reduced by 82% (specificity) compared to the test data. However, the recall was 36%, and further improvement is needed.

Original languageEnglish
Title of host publicationProceedings of 2021 International Conference on Machine Learning and Cybernetics, ICMLC 2021
PublisherIEEE Computer Society
ISBN (Electronic)9781665466080
DOIs
Publication statusPublished - 2021
Event20th International Conference on Machine Learning and Cybernetics, ICMLC 2021 - Adelaide, United States
Duration: Dec 4 2021Dec 5 2021

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume2021-December
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Conference

Conference20th International Conference on Machine Learning and Cybernetics, ICMLC 2021
Country/TerritoryUnited States
CityAdelaide
Period12/4/2112/5/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Human-Computer Interaction

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