Sparse Representation-Based Deformation Model for Atlas-Based Segmentation of Liver CT Images

摘要

Liver segmentation in computed tomography (CT) images is a fundamental step for various computer-assisted clinical applications. However, automatic liver segmentation from CT images is still a challenging task. In this paper, we propose a novel non-parametric sparse representation-based deformation model (SRDM) for atlas-based liver segmentation framework using nonrigid registration based on free-form deformations (FFDs) model. Specifically, during atlas-based segmentation procedure, our proposed SRDM provides a regularization for the resulting deformation that maps the atlas to the space of the target image, constraining it to be a sparse linear combination of existing training deformations in a deformation repository. We evaluated our proposed method based on a set of 30 contrast-enhanced abdominal CT images, resulting in superior performance when compared to state-of-the-art atlas-based segmentation methods.