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

• Material Type: 博士論文
• ISSN: 1349-6867
• Title: Accelerated organ region segmentation by the revised radial basis function network using a graphics processing unit
• Volume: 66 1-2
• Author/Editor: Konishi, Takeshi; Kondo, Tadashi; Moriguchi, Hiroki; Tagi, Masato; Hirose, Jun
• Author Heading: Konishi, Takeshi; Kondo, Tadashi; Moriguchi, Hiroki; Tagi, Masato; Hirose, Jun
• Publication, Distribution, etc.: Tokushima University Faculty of Medicine
• Publication Date: 2019-02
• Publication Date (W3CDTF): 2019-02
• Alternative Title: 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
• Periodical title: The Journal of Medical Investigation
• Degree grantor/type: 徳島大学
• Date Granted: 2018-11-22
• Date Granted (W3CDTF): 2018-11-22
• Dissertation Number: 甲第3243号
• Degree Type: 博士（医学）
• Conferring No. (Dissertation): 甲第3243号
• Text Language Code: eng
• Subject Heading: RBF networks; GPGPU; organ segmentation
• Target Audience: 一般
• Note (General): 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.
• DOI: info:doi/10.2152/jmi.66.86; https://doi.org/info:doi/10.2152/jmi.66.86
• Persistent ID (NDL): info:ndljp/pid/11344401; https://dl.ndl.go.jp/pid/11344401
• Collection: 障害者向け資料
• Collection (Materials For Handicapped People:1): テキストデータ
• Collection (particular): 国立国会図書館デジタルコレクション > デジタル化資料 > 博士論文; https://dl.ndl.go.jp/collections/A00014
• Acquisition Basis: 博士論文（自動収集）
• Date Accepted (W3CDTF): 2019-09-04T09:14:56+09:00
• Format (IMT): application/pdf
• Access Restrictions: 国立国会図書館内限定公開
• Service for the Digitized Contents Transmission Service: 図書館・個人送信対象外
• Availability of remote photoduplication service: 可
• Periodical Title (URI): https://repo.lib.tokushima-u.ac.jp/113295
• Data Provider (Database): 国立国会図書館 : 国立国会図書館デジタルコレクション; https://dl.ndl.go.jp