Accelerated organ region segmentation by the revised radial basis function network using a graphics processing unit

• 資料種別: 博士論文
• タイトル: Accelerated organ region segmentation by the revised radial basis function network using a graphics processing unit
• 著者・編者: Konishi, Takeshi; Kondo, Tadashi; Moriguchi, Hiroki; Tagi, Masato; Hirose, Jun
• 著者標目: Konishi, Takeshi; Kondo, Tadashi; Moriguchi, Hiroki; Tagi, Masato; Hirose, Jun
• 出版事項: Tokushima University Faculty of Medicine
• 出版年月日等: 2019-02
• 出版年（W3CDTF）: 2019-02
• 並列タイトル等: graphics processing unitを活用した改良型radial basis functionネットワークによる臓器領域描出の高速化; Accelerating revised RBF neural network
• タイトル（掲載誌）: The Journal of Medical Investigation
• 巻号年月日等（掲載誌）: 66 1-2
• 掲載巻: 66
• 掲載号: 1-2
• 掲載ページ: 86-92
• ISSN（掲載誌）: ISSN : 1349-6867; ISSN : 1343-1420
• 授与機関名: 徳島大学
• 授与年月日: 2018-11-22
• 報告番号: 甲第3243号
• 学位: 博士（医学）
• 本文の言語コード: eng
• 件名標目: RBF networks; GPGPU; organ segmentation
• 対象利用者: 一般
• 一般注記: This study aimed to accelerate the segmentation of organs in medical imaging with the revised radial basis function (RBF) network, using a graphics processing unit (GPU). We segmented the lung and liver regions from 250 chest x-ray computed tomography (CT) images and 160 abdominal CT images, respectively, using the revised RBF network. We compared the time taken to segment images and their accuracy between serial processing by a single-core central processing unit (CPU), parallel processing using four CPU cores, and GPU processing. Segmentation times for lung and liver organ regions shortened to 57.80 and 35.35 seconds for CPU parallel processing and 20.16 and 11.02 seconds for GPU processing, compared to 211.03 and 124.21 seconds for CPU serial processing, respectively. The concordance rate of the segmented region to the normal region in slices excluding the upper and lower ends (173 lung and 111 liver slices) was 98% for lung and 96% for liver. The use of CPU parallel processing and GPU shortened the organ segmentation time in the revised RBF network without compromising segmentation accuracy. In particular, segmentation time was shortened to less than 10% with GPU. This processing method will contribute to workload reduction in imaging analysis.
• 記録形式（IMT）: application/pdf
• 一次資料へのリンクURL: fulltext; https://repo.lib.tokushima-u.ac.jp/files/public/11/113295/20190729160713578728/k3243_abstract_review.pdf; https://repo.lib.tokushima-u.ac.jp/files/public/11/113295/2019073018165122575/k3243_fulltext.pdf
• オンライン閲覧公開範囲: インターネット公開
• 関連情報（DOI）: 10.2152/jmi.66.86
• 掲載誌（NCID）: AA12022913; https://ci.nii.ac.jp/ncid/AA12022913; AA11166929; https://ci.nii.ac.jp/ncid/AA11166929
• 連携機関・データベース: 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）; https://irdb.nii.ac.jp
• 提供元機関・データベース: 徳島大学 : 徳島大学機関リポジトリ