Minimal Paths for Tubular Structure Segmentation with Coherence Penalty and Adaptive Anisotropy

TL;DR

This paper introduces a novel minimal path model with coherence penalty and adaptive anisotropy for improved tubular structure segmentation, validated on synthetic and retinal images, outperforming existing methods.

Contribution

The paper presents a dynamic Riemannian metric with appearance coherence and anisotropy enhancement, improving tubular structure extraction in complex vessel trees.

Findings

Effective extraction of tubular structures from complex vessel trees.

Improved segmentation accuracy on synthetic and retinal images.

Promising quantitative validation results.

Abstract

The minimal path method has proven to be particularly useful and efficient in tubular structure segmentation applications. In this paper, we propose a new minimal path model associated with a dynamic Riemannian metric embedded with an appearance feature coherence penalty and an adaptive anisotropy enhancement term. The features that characterize the appearance and anisotropy properties of a tubular structure are extracted through the associated orientation score. The proposed dynamic Riemannian metric is updated in the course of the geodesic distance computation carried out by the efficient single-pass fast marching method. Compared to state-of-the-art minimal path models, the proposed minimal path model is able to extract the desired tubular structures from a complicated vessel tree structure. In addition, we propose an efficient prior path-based method to search for vessel radius value at each centerline position of the target. Finally, we perform the numerical experiments on both synthetic and real images. The quantitive validation is carried out on retinal vessel images. The results indicate that the proposed model indeed achieves a promising performance.