Deep vessel segmentation with joint multi-prior encoding

TL;DR

This paper introduces a novel joint prior encoding method that combines shape and topological information in a single latent space to improve the accuracy and consistency of 3D blood vessel segmentation in medical images.

Contribution

We propose a new joint prior encoding mechanism that integrates shape and topology priors into vessel segmentation models, enhancing anatomical accuracy.

Findings

Demonstrated improved segmentation accuracy on 3D-IRCADb dataset.

Enhanced anatomical consistency in vessel delineation.

Potential to advance deep priors encoding in medical imaging.

Abstract

The precise delineation of blood vessels in medical images is critical for many clinical applications, including pathology detection and surgical planning. However, fully-automated vascular segmentation is challenging because of the variability in shape, size, and topology. Manual segmentation remains the gold standard but is time-consuming, subjective, and impractical for large-scale studies. Hence, there is a need for automatic and reliable segmentation methods that can accurately detect blood vessels from medical images. The integration of shape and topological priors into vessel segmentation models has been shown to improve segmentation accuracy by offering contextual information about the shape of the blood vessels and their spatial relationships within the vascular tree. To further improve anatomical consistency, we propose a new joint prior encoding mechanism which incorporates both shape and topology in a single latent space. The effectiveness of our method is demonstrated on the publicly available 3D-IRCADb dataset. More globally, the proposed approach holds promise in overcoming the challenges associated with automatic vessel delineation and has the potential to advance the field of deep priors encoding.