A liver segmentation approach in contrast-enhanced CT images with patient-specific knowledge

In this work, we propose a shape-based liver segmentation approach using a patient specific knowledge. In which, we exploit the relation between consequent slices in multi-slice CT images to update the shape template that initially determined by the user. Then, the updated shape template is integrated with the graph cuts algorithm to segment the liver in each CT slice. The statistical parameters of the liver and non-liver tissues are initially determined according to the initial shape template and it is consequently updated from the nearby slices. The proposed approach does not require any prior training and it uses a single phase CT images; however, it is talented to deal with complex shape and intensity variations. The proposed approach is evaluated on 20 CT images with different kinds of liver abnormalities, tumors and cysts, and it achieves an average volumetric overlap error of 6.4% and average symmetric surface distance (ASD) of 0.8 compared to the manual segmentation.

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