Skip priors and add graph-based anatomical information, for point-based Couinaud segmentation

TL;DR

This paper introduces a point-based liver segmentation method that incorporates implicit anatomical information through graph reasoning, eliminating the need for explicit vessel structure prior, and achieves competitive results on public datasets.

Contribution

The novel approach adds a graph reasoning module to point-based segmentation, enabling implicit learning of liver vessel structures without explicit prior data.

Findings

Competitive Dice scores on MSD and LiTS datasets

Effective implicit anatomical information learning

Outperforms four prior point-based methods

Abstract

The preoperative planning of liver surgery relies on Couinaud segmentation from computed tomography (CT) images, to reduce the risk of bleeding and guide the resection procedure. Using 3D point-based representations, rather than voxelizing the CT volume, has the benefit of preserving the physical resolution of the CT. However, point-based representations need prior knowledge of the liver vessel structure, which is time consuming to acquire. Here, we propose a point-based method for Couinaud segmentation, without explicitly providing the prior liver vessel structure. To allow the model to learn this anatomical liver vessel structure, we add a graph reasoning module on top of the point features. This adds implicit anatomical information to the model, by learning affinities across point neighborhoods. Our method is competitive on the MSD and LiTS public datasets in Dice coefficient and average surface distance scores compared to four pioneering point-based methods. Our code is available at https://github.com/ZhangXiaotong015/GrPn.