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Proceedings Paper

Automatic anatomy partitioning of the torso region on CT images by using a deep convolutional network with majority voting
Author(s): Xiangrong Zhou; Takuya Kojima; Song Wang; Xinxin Zhou; Takeshi Hara; Taiki Nozaki; Masaki Matsusako; Hiroshi Fujita
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Paper Abstract

We propose an automatic approach to anatomy partitioning on three-dimensional (3D) computed tomography (CT) images that divides the human torso into several volumes of interest (VOIs) according to anatomical definition. In the proposed approach, a deep convolutional neural network (CNN) is trained to automatically detect the bounding boxes of organs on two-dimensional (2D) sections of CT images. The coordinates of those boxes are then grouped so that a vote on a 3D VOI (called localization) for each organ can be obtained separately. We applied this approach to localize the 3D VOIs of 17 types of organs in the human torso and then evaluated the performance of the approach by conducting a four-fold crossvalidation using a dataset consisting of 240 3D CT scans with the human-annotated ground truth for each organ region. The preliminary results showed that 86.7% of the 3D VOIs of the 3177 organs in the 240 test CT images were localized with acceptable accuracy (mean of Jaccard indexes was 72.8%) compared to that of the human annotations. This performance was better than that of the state-of-the-art method reported recently. The experimental results demonstrated that using a deep CNN for anatomy partitioning on 3D CT images was more efficient and useful compared to the method used in our previous work.

Paper Details

Date Published: 13 March 2019
PDF: 6 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109500Z (13 March 2019); doi: 10.1117/12.2512651
Show Author Affiliations
Xiangrong Zhou, Gifu Univ. (Japan)
Takuya Kojima, Gifu Univ. (Japan)
Song Wang, Univ. of South Carolina (United States)
Xinxin Zhou, Nagoya Bunri Univ. (Japan)
Takeshi Hara, Gifu Univ. (Japan)
Taiki Nozaki, St. Luke’s International Hospital (Japan)
Masaki Matsusako, St. Luke's International Hospital (Japan)
Hiroshi Fujita, Gifu Univ. (Japan)

Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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