Anatomical-Aware Point-Voxel Network for Couinaud Segmentation in Liver CT

Xukun Zhang, Yang Liu, Sharib Ali, Xiao Zhao, Mingyang Sun, Minghao Han, Tao Liu, Peng Zhai, Zhiming Cui, Peixuan Zhang, Xiaoying Wang, Lihua Zhang

Abstract: Accurately segmenting the liver into anatomical segments is crucial for surgical planning and lesion monitoring in CT imaging. However, this is a challenging task as it is defined based on vessel structures, and there is no intensity contrast between adjacent segments in CT images. In this paper, we propose a novel point-voxel fusion framework to address this challenge. Specifically, we first segment the liver and vessels from the CT image, and generate 3D liver point clouds and voxel grids embedded with vessel structure prior. Then, we design a multi-scale point-voxel fusion network to capture the anatomical structure and semantic information of the liver and vessels, respectively, while also increasing important data access through vessel structure prior. Finally, the network outputs the classification of Couinaud segments in the continuous liver space, producing a more accurate and smooth 3D Couinaud segmentation mask. Our proposed method outperforms several state-of-the-art methods, both point-based and voxel-based, as demonstrated by our experimental results on two public liver datasets. Code, datasets, and models are released at https://github.com/xukun-zhang/Couinaud-Segmentation.