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Proceedings Paper

Automatic anatomy partitioning of the torso region on CT images by using multiple organ localizations with a group-wise calibration technique
Author(s): Xiangrong Zhou; Syoichi Morita; Xinxin Zhou; Huayue Chen; Takeshi Hara; Ryujiro Yokoyama; Masayuki Kanematsu; Hiroaki Hoshi; Hiroshi Fujita
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Paper Abstract

This paper describes an automatic approach for anatomy partitioning on three-dimensional (3D) computedtomography (CT) images that divide the human torso into several volume-of-interesting (VOI) images based on anatomical definition. The proposed approach combines several individual detections of organ-location with a groupwise organ-location calibration and correction to achieve an automatic and robust multiple-organ localization task. The essence of the proposed method is to jointly detect the 3D minimum bounding box for each type of organ shown on CT images based on intra-organ-image-textures and inter-organ-spatial-relationship in the anatomy. Machine-learning-based template matching and generalized Hough transform-based point-distribution estimation are used in the detection and calibration processes. We apply this approach to the automatic partitioning of a torso region on CT images, which are divided into 35 VOIs presenting major organ regions and tissues required by routine diagnosis in clinical medicine. A database containing 4,300 patient cases of high-resolution 3D torso CT images is used for training and performance evaluations. We confirmed that the proposed method was successful in target organ localization on more than 95% of CT cases. Only two organs (gallbladder and pancreas) showed a lower success rate: 71 and 78% respectively. In addition, we applied this approach to another database that included 287 patient cases of whole-body CT images scanned for positron emission tomography (PET) studies and used for additional performance evaluation. The experimental results showed that no significant difference between the anatomy partitioning results from those two databases except regarding the spleen. All experimental results showed that the proposed approach was efficient and useful in accomplishing localization tasks for major organs and tissues on CT images scanned using different protocols.

Paper Details

Date Published: 20 March 2015
PDF: 6 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94143K (20 March 2015); doi: 10.1117/12.2081786
Show Author Affiliations
Xiangrong Zhou, Gifu Univ. School of Medicine (Japan)
Syoichi Morita, Gifu Univ. School of Medicine (Japan)
Xinxin Zhou, Nagoya Bunri Univ. (Japan)
Huayue Chen, Gifu Univ. School of Medicine (Japan)
Takeshi Hara, Gifu Univ. School of Medicine (Japan)
Ryujiro Yokoyama, Gifu Univ. School of Medicine (Japan)
Masayuki Kanematsu, Gifu Univ. Hospital (Japan)
Hiroaki Hoshi, Gifu Univ. School of Medicine (Japan)
Hiroshi Fujita, Gifu Univ. School of Medicine (Japan)

Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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