Topo-UNet: A topology-aware multi-task network for pulmonary vessel segmentation.

The precise segmentation of pulmonary vessels is crucial for the early diagnosis and treatment of pulmonary diseases. However, vessel images are frequently compromised by high levels of noise and blurred boundaries, which complicate the extraction of vessel features. Current state-of-the-art (SOTA) methods also encounter challenges such as segmenting fine vessels, interruptions in vessel continuity, and loss of inter-layer information. To address these issues, this study proposes a topology-aware multi-task network called Topo-UNet, which integrates the Bidirectional Slice-wise ConvLSTM (BS-ConvLSTM) module and topology-aware auxiliary task to enhance the accurate capture of vessel structural features. The BS-ConvLSTM module mitigates discontinuities in vessel structures by extracting spatial continuity features. Meanwhile, the topology-aware auxiliary task employs a Gaussian function to simulate the intensity distribution within vessels, improving the network's capability to accurately identify vessel structures. Additionally, this study introduces a joint auxiliary task-based method for vessel refinement that increases the recognition rate of fine vessels while enhancing segmentation continuity. Extensive experiments were conducted on CT and CTA datasets to evaluate the performance of Topo-UNet. Comparisons with various SOTA methods across multiple metrics show that Topo-UNet demonstrates superior performance in the task of pulmonary vessel segmentation. Specifically, it achieved Dice coefficients of 90.78% and 91.91%, along with Intersection over Union (IoU) scores of 83.31% and 85.09% across two test datasets. Furthermore, the discussion section presents a grouping evaluation strategy to address the segmentation performance of vessels of varying sizes, and explores a quadratic approach for vessel refinement, enhancing the segmentation of fine vessels. The code of the proposed Topo-UNet is publicly available at https://github.com/liu66-git/Topo-UNet.