It is widely known that morphological changes of the liver and the spleen occur during the clinical course of chronic liver diseases. In this paper, we proposed a morphological analysis method based on statistical shape models (SSMs) of the liver and spleen for computer-aided diagnosis and quantification of the chronic liver. We constructed not only the liver SSM but also the spleen SSM and a joint SSM of the liver and the spleen for a morphologic analysis of the cirrhotic liver in CT images. The effective modes are selected based on both its accumulation contribution rate and its correlation with doctor's opinions (stage labels). We then learn a mapping function between the selected mode and the stage of chronic liver. The mapping function was used for diagnosis and staging of chronic liver diseases.
Random walks-based (RW) segmentation methods have been proven to have a potential application in segmenting the medical image with minimal interactive guidance. However, the approach leads to large-scale graphs due to number of nodes equal to voxel number. Also, segmentation is inaccurate because of the unavailability of appropriate initial seed points. It is a challenge to use the RW-based segmentation algorithm to segment organ regions from 3D medical images interactively. In this paper, a knowledge-based segmentation framework for multiple organs is proposed based on random walks. This method employs the previous segmented slice as prior knowledge (the shape and intensity constraints) for automatic segmentation of other slices, which can reduce the graph scale and significantly speed up the optimization procedure of the graph. To assess the efficiency of our proposed method, experiments were performed on liver tissues, spleen tissues and hepatic cancer and it was extensively evaluated both quantitatively and qualitatively. Comparing our method with conventional RW and state-of-the-art interactive segmentation methods, our results show an improvement in the accuracy for multi-organ segmentation (p < 0.001).
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