A new segmentation framework based on sparse shape composition in liver surgery planning system.

PURPOSE: To improve the accuracy and the robustness of the segmentation in living donor liver transplantation (LDLT) surgery planning system, the authors present a new segmentation framework that addresses challenges induced by the complex shape variations of patients' livers with cancer. It is designed to achieve the accurate and robust segmentation of hepatic parenchyma, portal veins, hepatic veins, and tumors in the LDLT surgery planning system.
METHODS: The segmentation framework proposed in this paper includes two important modules: (1) The robust shape prior modeling for liver, in which the sparse shape composition (SSC) model is employed to deal with the complex variations of liver shapes and obtain patient-specific liver shape priors. (2) The integration of the liver shape prior with a minimally supervised segmentation algorithm to achieve the accurate segmentation of hepatic parenchyma, portal veins, hepatic veins, and tumors simultaneously. The authors apply this segmentation framework to our previously developed LDLT surgery planning system to enhance its accuracy and robustness when dealing with complex cases of patients with liver cancer.
RESULTS: Compared with the principal component analysis, the SSC model shows a great advantage in handling the complex variations of liver shapes. It also effectively excludes gross errors and outliers that appear in the input shape and preserves local details for specific patients. The proposed segmentation framework was evaluated on the clinical image data of liver cancer patients, and the average symmetric surface distance for hepatic parenchyma, portal veins, hepatic veins, and tumors was 1.07 ± 0.76, 1.09 ± 0.28, 0.92 ± 0.35 and 1.13 ± 0.37 mm, respectively. The Hausdorff distance for these four tissues was 7.68, 4.67, 4.09, and 5.36 mm, respectively.
CONCLUSIONS: The proposed segmentation framework improves the robustness of the LDLT surgery planning system remarkably when dealing with complex clinical liver shapes. The SSC model is able to handle non-Gaussian errors and preserve local detail information of the input liver shape. As a result, the proposed framework effectively addresses the problems caused by the complex shape variations of livers with cancer. Our framework not only obtains accurate segmentation results for healthy persons and common patients, but also shows high robustness when dealing with specific patients with large variations of liver shapes in complex clinical environments.