Identification of intracerebral hematoma and intraventricular hemorrhage in thick-slice CT images of the head using 2D-CNN

• Published in: Proceedings of the Annual Conference of Biomedical Fuzzy Systems Association Vol. 35; p. A-2
• Main Authors: ARIMURA, Koichi, FUJITA, Daisuke, Kobashi, Syoji, OKA, Kazunori, IIHARA, Koji
• Format: Journal Article
• Language: Japanese
• Published: Biomedical Fuzzy Systems Association 2022; バイオメディカル・ファジィ・システム学会
• Subjects: convolutional neural network; CT; intracerebral hematoma; region extraction
• ISSN: 1345-1510; 2424-2586
• DOI: 10.24466/pacbfsa.35.0_A-2

Intracerebral hematoma (ICH) is a disease with high mortality and poor prognosis rate, accounting for approximately 10% of all cerebrovascular disease. Manual extraction of ICH regions lacks accuracy and speed, and a quantitative evaluation method is needed. In this study, we propose a method that divides the extraction of ICH regions into multiple stages and extracts the target using two-class classification based on convolutional neural network. The performance of the model is evaluated using 18 subjects with intraventricular hemorrhage, and it is shown that the proposed method is promising for the extraction of ICH regions in a region with high absorption rates.