18 March 2014 A universal approach for automatic organ segmentations on 3D CT images based on organ localization and 3D GrabCut
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Abstract
This paper describes a universal approach to automatic segmentation of different internal organ and tissue regions in three-dimensional (3D) computerized tomography (CT) scans. The proposed approach combines object localization, a probabilistic atlas, and 3D GrabCut techniques to achieve automatic and quick segmentation. The proposed method first detects a tight 3D bounding box that contains the target organ region in CT images and then estimates the prior of each pixel inside the bounding box belonging to the organ region or background based on a dynamically generated probabilistic atlas. Finally, the target organ region is separated from the background by using an improved 3D GrabCut algorithm. A machine-learning method is used to train a detector to localize the 3D bounding box of the target organ using template matching on a selected feature space. A content-based image retrieval method is used for online generation of a patient-specific probabilistic atlas for the target organ based on a database. A 3D GrabCut algorithm is used for final organ segmentation by iteratively estimating the CT number distributions of the target organ and backgrounds using a graph-cuts algorithm. We applied this approach to localize and segment twelve major organ and tissue regions independently based on a database that includes 1300 torso CT scans. In our experiments, we randomly selected numerous CT scans and manually input nine principal types of inner organ regions for performance evaluation. Preliminary results showed the feasibility and efficiency of the proposed approach for addressing automatic organ segmentation issues on CT images.
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Xiangrong Zhou, Takaaki Ito, Xinxin Zhou, Huayue Chen, Takeshi Hara, Ryujiro Yokoyama, Masayuki Kanematsu, Hiroaki Hoshi, Hiroshi Fujita, "A universal approach for automatic organ segmentations on 3D CT images based on organ localization and 3D GrabCut", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90352V (18 March 2014); doi: 10.1117/12.2043507; https://doi.org/10.1117/12.2043507
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