Blood vessel-based liver segmentation through the portal phase of a CT dataset

Ahmed S. Maklad; Mikio Matsuhiro; Hidenobu Suzuki; Yoshiki Kawata; Noboru Niki; Noriyuki Moriyama; Toru Utsunomiya; Mitsuo Shimada

doi:10.1117/12.2007546

26 February 2013 Blood vessel-based liver segmentation through the portal phase of a CT dataset

Ahmed S. Maklad, Mikio Matsuhiro, Hidenobu Suzuki, Yoshiki Kawata, Noboru Niki, Noriyuki Moriyama, Toru Utsunomiya, Mitsuo Shimada

Author Affiliations +

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 86700X (2013) https://doi.org/10.1117/12.2007546
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

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.

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Ahmed S. Maklad, Mikio Matsuhiro, Hidenobu Suzuki, Yoshiki Kawata, Noboru Niki, Noriyuki Moriyama, Toru Utsunomiya, and Mitsuo Shimada "Blood vessel-based liver segmentation through the portal phase of a CT dataset", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86700X (26 February 2013); https://doi.org/10.1117/12.2007546

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