Continuous and complete liver vessel segmentation with graph-attention guided diffusion
This work addresses a critical problem in medical imaging for liver disease diagnosis and treatment planning, though it is incremental as it builds on existing diffusion models with novel adaptations.
The paper tackles the challenge of improving connectivity and completeness in liver vessel segmentation, particularly for small vessels, by integrating a graph-attention module into a diffusion-based model to enhance geometry learning and multi-scale focus, achieving state-of-the-art performance on two public datasets.
Improving connectivity and completeness are the most challenging aspects of liver vessel segmentation, especially for small vessels. These challenges require both learning the continuous vessel geometry, and focusing on small vessel detection. However, current methods do not explicitly address these two aspects and cannot generalize well when constrained by inconsistent annotations. Here, we take advantage of the generalization of the diffusion model and explicitly integrate connectivity and completeness in our diffusion-based segmentation model. Specifically, we use a graph-attention module that adds knowledge about vessel geometry, and thus adds continuity. Additionally, we perform the graph-attention at multiple-scales, thus focusing on small liver vessels. Our method outperforms eight state-of-the-art medical segmentation methods on two public datasets: 3D-ircadb-01 and LiVS. Our code is available at https://github.com/ZhangXiaotong015/GATSegDiff.