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

Abstract

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.

Automated compromised right lung segmentation method using a robust atlas-based active volume model with sparse shape composition prior in CT

“…Sparsity has been widely employed for medical image analysis tasks, such as segmentation, registration and reconstruction [6][7][8][9][10][11][12][13][14][15][16]. The main contribution the work on which we report here is threefold: (1) To address the challenge of the original ground truth for the compromised right lung, we generate the robust shape atlas with the refined ground truth from the output of SSC.…”

A homotopy-based sparse representation for fast and accurate shape prior modeling in liver surgical planning

“…Due to these advantages, SSC has been successfully applied in cardiac motion analysis (Yu et al, 2013), lung localization and other applications (Zhang et al, 2012a). It also showed a great advantage in robust liver shape modeling (Wang et al, 2013). However, its computational efficiency may be limited by increasing repository's capacity and number of vertices on each shape.…”

Statistical shape model reconstruction with sparse anomalous deformations: Application to intervertebral disc herniation

“…Sparse shape composition of Zhang et al [10] represents a test shape instance as a sparse linear combination of training data and defines regions of specific local deformations. It has been successfully applied in several challenging medical image segmentation problems, such as lung localization and liver segmentation [10,11] or cardiac motion analysis [12], where modeling of previously unseen local shape deformations is required. Further improvements in medical image segmentation were achieved by combining sparse reconstructions of shape subregions [13,14] and by sparse modeling of the signal intensity appearance in the form of active appearance models [14].…”