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Proceedings Paper

Blood vessel-based liver segmentation through the portal phase of a CT dataset
Author(s): Ahmed S. Maklad; Mikio Matsuhiro; Hidenobu Suzuki; Yoshiki Kawata; Noboru Niki; Noriyuki Moriyama; Toru Utsunomiya; Mitsuo Shimada
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Paper Abstract

Blood vessels are dispersed throughout the human body organs and carry unique information for each person. This information can be used to delineate organ boundaries. The proposed method relies on abdominal blood vessels (ABV) to segment the liver considering the potential presence of tumors through the portal phase of a CT dataset. ABV are extracted and classified into hepatic (HBV) and nonhepatic (non-HBV) with a small number of interactions. HBV and non-HBV are used to guide an automatic segmentation of the liver. HBV are used to individually segment the core region of the liver. This region and non-HBV are used to construct a boundary surface between the liver and other organs to separate them. The core region is classified based on extracted posterior distributions of its histogram into low intensity tumor (LIT) and non-LIT core regions. Non-LIT case includes normal part of liver, HBV, and high intensity tumors if exist. Each core region is extended based on its corresponding posterior distribution. Extension is completed when it reaches either a variation in intensity or the constructed boundary surface. The method was applied to 80 datasets (30 Medical Image Computing and Computer Assisted Intervention (MICCAI) and 50 non-MICCAI data) including 60 datasets with tumors. Our results for the MICCAI-test data were evaluated by sliver07 [1] with an overall score of 79.7, which ranks seventh best on the site (December 2013). This approach seems a promising method for extraction of liver volumetry of various shapes and sizes and low intensity hepatic tumors.

Paper Details

Date Published: 26 February 2013
PDF: 7 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86700X (26 February 2013); doi: 10.1117/12.2007546
Show Author Affiliations
Ahmed S. Maklad, Univ. of Tokushima (Japan)
Mikio Matsuhiro, Univ. of Tokushima (Japan)
Hidenobu Suzuki, Univ. of Tokushima (Japan)
Yoshiki Kawata, Univ. of Tokushima (Japan)
Noboru Niki, Univ. of Tokushima (Japan)
Noriyuki Moriyama, National Cancer Ctr. (Japan)
Toru Utsunomiya, Univ. of Tokushima (Japan)
Mitsuo Shimada, Univ. of Tokushima (Japan)

Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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