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.
X. Zhang and Y. Liu—Contributed equally.
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Acknowledgement
This project was funded by the National Natural Science Foundation of China (82090052, 82090054, 82001917 and 81930053), Clinical Research Plan of Shanghai Hospital Development Center (No. 2020CR3004A), and National Key Research and Development Program of China under Grant (2021YFC2500402).
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Zhang, X. et al. (2023). Anatomical-Aware Point-Voxel Network for Couinaud Segmentation in Liver CT. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14222. Springer, Cham. https://doi.org/10.1007/978-3-031-43898-1_45
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