ムラガキ ヨシヒロ
MURAGAKI Yoshihiro
村垣 善浩 所属 医学部 医学科(東京女子医科大学病院) 職種 客員教授 |
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論文種別 | 原著 |
言語種別 | 英語 |
査読の有無 | 査読あり |
表題 | Computerized Classification Method for Molecular Subtypes in Glioma with Multi-Scale 3D-Attention Branch Networks Analyzing Multi-Sequence Brain MRI Images |
掲載誌名 | 正式名:IEEJ Transactions on Electrical and Electronic Engineering ISSNコード:19314973 |
掲載区分 | 国内 |
出版社 | 一般社団法人 電気学会 |
巻・号・頁 | 143(5),pp.539-545 |
著者・共著者 | TANAKA Daiki† , HIZUKURI Akiyoshi, NAKAYAMA Ryohei, KUSUDA Kaori, MASAMUNE Ken, MURAGAKI Yoshihiro |
発行年月 | 2023/05/01 |
概要 | <p>The purpose of this study was to develop a computerized classification method for molecular subtypes in low-grade gliomas (LGGs) with multi-scale 3D-at-tention branch networks analyzing multi-sequence brain MRI images. Our dataset consisted of brain T1-weighted and T2-weighted MRI im-ages for 217 patients (58 Astrocytoma IDH-mutant, 49 Astrocytoma IDH-wildtype, and 110 Oligodendroglioma). The proposed method was constructed from a feature extractor, an attention branch, and a perception branch. In the feature extractor, the feature maps were extracted from brain T1-weighted and T2-weighted MRI images, respectively. The attention branch focused on a tumor region and generated the attention maps normalized to 0.0 - 1.0. The feature maps were then multiplied by the attention maps to weight features on LGG in the feature maps. The molecular subtype in LGG was evaluated in the perception branch. The classification accuracy for the proposed method was 63.6%, showing an improvement when compared with the conven-tional method using only single sequence (T2-weighted) MRI images (59.9%).</p> |
DOI | 10.1541/ieejeiss.143.539 |