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Chengwen Chu

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Proceedings Article | 13 March 2013 Paper

Multi-organ segmentation from 3D abdominal CT images using patient-specific weighted-probabilistic atlas

Chengwen Chu, Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara, Yuichiro Hayashi, Robin Wolz, Daniel Rueckert, Kensaku Mori

Proceedings Volume 8669, 86693Y (2013) https://doi.org/10.1117/12.2007601

KEYWORDS: Image segmentation, Pancreas, Liver, Databases, Kidney, Image processing, Spleen, Computed tomography, Expectation maximization algorithms, 3D image processing

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Organ segmentation of CT volumes is a basic function of computer-aided diagnosis and surgery-assistance systems. Many of these systems implement organ segmentation methods that are limited to specific organs and that are not robust in dealing with inter-subject differences of organ shape or position. In this paper, we propose an automated method for multi-organ segmentation of abdominal 3D CT volumes by using a patient-specific, weighted-probabilistic atlas for organ position. This is achieved in a two-step process. First, we prepare for segmentation by dividing an atlas database into multiple clusters. This is done using pairs of a training image and the corresponding manual segmentation data set. In the next step, we choose a cluster whose template image is the most similar to the target image. We then weight all of the atlases in the selected cluster by calculating the similarities between the atlases and the target image to dynamically generate a specific probabilistic atlas for each target image. We use the generated probabilistic atlas in MAP estimation to obtain a rough segmentation result and then refine it by using a graph-cut method. Our method can simultaneously segment four organs: the liver, spleen, pancreas and kidneys. Our weighting scheme greatly reduces segmentation error due to inter-subject differences. We applied our method to 100 cases of CT volumes and thus showed that it could segment the liver, spleen, pancreas and kidneys with Dice similarity coefficients of 95.2%, 89.7%, 69.6%, and 89.4%, respectively.

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