二段階のRandom Forestを用いた血中循環がん細胞の検出

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2024/03/01

タイトル(英語): Detection of Circulating Tumor Cells in Blood Using Two-Step Random Forest

著者名: 魏　樺（東京理科大学 先進工学部），名取　隆廣（東海大学 文理融合学部 人間情報工学科），田中　智博（岡山大学 学術研究院 医歯薬学域），青木　伸（東京理科大学 薬学部），栗山　翔（日本医科大学 消化器外科），山田　岳史（日本医科大学 消化器外科），相川　直幸（東京理科大学 先進工学部）

著者名(英語): Hua Wei (Faculty of Advanced Engineering, Tokyo University of Science), Takahiro Natori (Department of Human Information Engineering, School of Humanities and Science, Tokai University), Tomohiro Tanaka (Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University), Shin Aoki (Faculty of Pharmaceutical Sciences, Tokyo University of Science), Sho Kuriyama (Department of Gastrointestinal and Hepato-Billiary-Pancreatic Surgery, Nippon Medical School), Takeshi Yamada (Department of Gastrointestinal and Hepato-Billiary-Pancreatic Surgery, Nippon Medical School), Naoyuki Aikawa (Faculty of Advanced Engineering, Tokyo University of Science)

キーワード: 血中循環がん細胞，自動検出，二段階分類，機械学習，形状特徴量，表面特徴量　　CTCs，auto detection，two-step classification，machine learning，shape feature，surface feature

要約(英語): Cancer has been the leading cause of death among Japanese since 1981, and many people die from it every year worldwide. While various measures have been taken to reduce the mortality rate of cancer, circulating tumor cells (CTCs) in the blood have been attracting attention in recent years. In the past, CTCs were detected by visual inspection by a physician or by an expensive machine, but these methods required much effort by the physician and required only EpCAM-expressing cells to be detected. In addition, detection by image processing has been used, but it has the problem that the area of interest is only a part of the area and there are many false positives. In this paper, we propose a two-step classification method that focuses on the shape and surface of cells. In the proposed method, multiple shape and surface features are obtained for four types of cells in blood images: Clusters, CTCs, Normal Cells, and Vertical Cells. Based on the features, cells are classified using a two-step Random Forest and their accuracy is evaluated. Furthermore, the effectiveness of the proposed method is demonstrated by comparing it with conventional methods.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.144 No.3 （2024） 特集Ⅰ：スマートシステムと計測・制御技術　特集Ⅱ：シリコンならびにワイドバンドギャップパワー半導体の最新技術

本誌掲載ページ: 121-126 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/144/3/144_121/_article/-char/ja/