クスダ　カオリ   Kusuda Kaori   楠田　佳緒    所属   医学研究科 医学研究科 (医学部医学科をご参照ください)    職種   非常勤講師
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