Dynamic Snake Convolution based on Topological Geometric Constraints for Tubular Structure Segmentation (2307.08388v2)

Abstract: Accurate segmentation of topological tubular structures, such as blood vessels and roads, is crucial in various fields, ensuring accuracy and efficiency in downstream tasks. However, many factors complicate the task, including thin local structures and variable global morphologies. In this work, we note the specificity of tubular structures and use this knowledge to guide our DSCNet to simultaneously enhance perception in three stages: feature extraction, feature fusion, and loss constraint. First, we propose a dynamic snake convolution to accurately capture the features of tubular structures by adaptively focusing on slender and tortuous local structures. Subsequently, we propose a multi-view feature fusion strategy to complement the attention to features from multiple perspectives during feature fusion, ensuring the retention of important information from different global morphologies. Finally, a continuity constraint loss function, based on persistent homology, is proposed to constrain the topological continuity of the segmentation better. Experiments on 2D and 3D datasets show that our DSCNet provides better accuracy and continuity on the tubular structure segmentation task compared with several methods. Our codes will be publicly available.

• The paper introduces DSCNet, integrating dynamic snake convolution, multi-view feature fusion, and topological loss for improved tubular segmentation.
• Dynamic Snake Convolution adapts to complex morphological features, enabling precise segmentation of slender and tortuous structures.
• Experimental results on datasets like DRIVE show DSCNet achieves superior performance with an 82.06% Dice score in vessel segmentation.

Dynamic Snake Convolution based on Topological Geometric Constraints for Tubular Structure Segmentation

The paper "Dynamic Snake Convolution based on Topological Geometric Constraints for Tubular Structure Segmentation" presents a novel framework aimed at improving the segmentation accuracy of tubular structures. These structures, including blood vessels and roads, are critical in various domains but challenging to segment due to their slender morphologies and complex global configurations.

Methodological Overview

The authors introduce DSCNet, which enhances perception across three stages: feature extraction, feature fusion, and loss constraint.

1. Dynamic Snake Convolution (DSConv): This convolutional approach targets the geometric intricacies of tubular structures by dynamically adapting to the morphological features, allowing for precise segmentation. Unlike conventional deformable convolutions, DSConv incorporates topological insights to enhance focus on thin, tortuous segments.
2. Multi-view Feature Fusion Strategy: The strategy generates multiple morphological kernel templates, capturing essential features from diverse perspectives. It employs a random drop strategy during training to mitigate redundant noise and enhance segmentation efficiency.
3. Topological Continuity Constraint Loss (TCLoss): TCLoss is derived from Persistent Homology, guiding the network to address segmentation continuity. By focusing on fracture regions and employing Hausdorff distance calculations, this approach ensures a topological perspective in guiding network learning.

Experimental Results

The authors conducted evaluations on multiple datasets, including the 2D DRIVE and Massachusetts Roads datasets, and a 3D Cardiac CCTA dataset. The results demonstrated DSCNet's superiority in volumetric accuracy, topological continuity, and reduced distance errors. Specifically, in DRIVE, the model achieved Dice scores of 82.06%, outperforming existing methods.

Implications and Future Directions

The integration of geometric and topological constraints presents a significant advance in segmentation tasks where morphology plays a vital role. The use of domain-specific knowledge provides a tailored approach, potentially adaptable to other morphological structures. Future research might examine the extension of this framework to other tubular structures, exploring additional domain-specific integrations or topological methods to further refine segmentation performance.

Overall, this research offers a comprehensive solution addressing both the morphological complexity and continuity challenges inherent in tubular structure segmentation, providing an effective tool for both medical and remote sensing applications.