Topology-Preserving 3D Image Segmentation Based On Hyperelastic Regularization

Abstract

Image segmentation is to extract meaningful objects from a given image. For degraded images due to occlusions, obscurities or noises, the accuracy of the segmentation result can be severely affected. To alleviate this problem, prior information about the target object is usually introduced. In [10], a topology-preserving registration-based segmentation model was proposed, which is restricted to segment 2D images only. In this paper, we propose a novel 3D topologypreserving registration-based segmentation model with the hyperelastic regularization, which can handle both 2D and 3D images. The existence of the solution of the proposed model is established. We also propose a converging iterative scheme to solve the proposed model. Numerical experiments have been carried out on the synthetic and real images, which demonstrate the effectiveness of our proposed model.

Inter and Intra Slice Reduction in Brain Tumor Segmentation

“…A novel differentiable search structure has been devised using a versatile network topology for facilitating a rapid gradient-based search [9]. As a result of it, to address this type of issue a topology loss has been implemented.…”