Automatic anatomy partitioning of the torso region on CT images by using a deep convolutional network with majority voting

Xiangrong Zhou; Takuya Kojima; Song Wang; Xinxin Zhou; Takeshi Hara; Taiki Nozaki; Masaki Matsusako; Hiroshi Fujita

doi:10.1117/12.2512651

13 March 2019 Automatic anatomy partitioning of the torso region on CT images by using a deep convolutional network with majority voting

Xiangrong Zhou, Takuya Kojima, Song Wang, Xinxin Zhou, Takeshi Hara, Taiki Nozaki, Masaki Matsusako, Hiroshi Fujita

Author Affiliations +

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109500Z (2019) https://doi.org/10.1117/12.2512651
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

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Xiangrong Zhou, Takuya Kojima, Song Wang, Xinxin Zhou, Takeshi Hara, Taiki Nozaki, Masaki Matsusako, and Hiroshi Fujita "Automatic anatomy partitioning of the torso region on CT images by using a deep convolutional network with majority voting", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109500Z (13 March 2019); https://doi.org/10.1117/12.2512651

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