English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Accurate segmentation of topological tubular structures, such as bloodvessels and roads, is crucial in various fields, ensuring accuracy andefficiency 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 guideour DSCNet to simultaneously enhance perception in three stages: featureextraction, feature fusion, and loss constraint. First, we propose a dynamicsnake convolution to accurately capture the features of tubular structures byadaptively focusing on slender and tortuous local structures. Subsequently, wepropose a multi-view feature fusion strategy to complement the attention tofeatures from multiple perspectives during feature fusion, ensuring theretention of important information from different global morphologies. Finally,a continuity constraint loss function, based on persistent homology, isproposed to constrain the topological continuity of the segmentation better.Experiments on 2D and 3D datasets show that our DSCNet provides better accuracyand continuity on the tubular structure segmentation task compared with severalmethods. Our codes will be publicly available.

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