School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
Med Phys. 2013 May;40(5):051913. doi: 10.1118/1.4802215.
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.
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.
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.
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.
为了提高活体肝移植（LDLT）手术规划系统中分割的准确性和鲁棒性，作者提出了一种新的分割框架，该框架解决了由癌症患者肝脏复杂形状变化引起的挑战。该框架旨在实现 LDLT 手术规划系统中肝实质、门静脉、肝静脉和肿瘤的准确和鲁棒分割。
本文提出的分割框架包括两个重要模块：（1）肝脏的稳健形状先验建模，其中稀疏形状组成（SSC）模型用于处理肝脏形状的复杂变化，获得患者特异性的肝脏形状先验。（2）将肝脏形状先验与最小监督分割算法相结合，实现肝实质、门静脉、肝静脉和肿瘤的精确分割。作者将该分割框架应用于我们之前开发的 LDLT 手术规划系统中，以增强其在处理复杂肝癌患者病例时的准确性和鲁棒性。
与主成分分析相比，SSC 模型在处理肝脏形状的复杂变化方面具有很大优势。它还可以有效地排除输入形状中的粗大误差和离群值，并保留特定患者的局部细节。该分割框架在肝癌患者的临床图像数据上进行了评估，肝实质、门静脉、肝静脉和肿瘤的平均对称表面距离分别为 1.07±0.76、1.09±0.28、0.92±0.35 和 1.13±0.37mm，Hausdorff 距离分别为 7.68、4.67、4.09 和 5.36mm。
该分割框架显著提高了 LDLT 手术规划系统处理复杂临床肝脏形状时的鲁棒性。SSC 模型能够处理非高斯误差并保留输入肝脏形状的局部细节信息。因此，该框架有效地解决了由癌症患者肝脏复杂形状变化引起的问题。我们的框架不仅能够获得健康人和常见患者的准确分割结果，而且在处理复杂临床环境中肝脏形状变化较大的特定患者时，也具有很高的鲁棒性。
