ID | 113295 |
タイトル別表記 | 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
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著者 |
小西, 健史
徳島大学大学院医科学教育部(医学専攻)
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キーワード | RBF networks
GPGPU
organ segmentation
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資料タイプ |
学位論文
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抄録 | 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.
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掲載誌名 |
The Journal of Medical Investigation
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ISSN | 13496867
13431420
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cat書誌ID | AA12022913
AA11166929
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出版者 | Tokushima University Faculty of Medicine
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巻 | 66
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号 | 1-2
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開始ページ | 86
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終了ページ | 92
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並び順 | 86
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発行日 | 2019-02
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備考 | 内容要旨・審査要旨・論文本文の公開
本論文は,著者Takeshi Konishiの学位論文として提出され,学位審査・授与の対象となっている。 |
EDB ID | |
出版社版DOI | |
出版社版URL | |
フルテキストファイル | |
言語 |
eng
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著者版フラグ |
博士論文全文を含む
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文科省報告番号 | 甲第3243号
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学位記番号 | 甲医第1392号
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学位授与年月日 | 2018-11-22
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学位名 |
博士(医学)
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学位授与機関 |
徳島大学
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部局 |
医学系
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