Enhancing Guide-Wire Segmentation with Multi-Morphological Features
Zixi Jia, Hongzhen Chen, Bin Li, Hui Nee Tang, Shoujun Zhou · 2025
Guide-wire segmentation is of great significance for robotic vascular interventions. However, due to its curvilinear structure and weak contrast with the image background, the accurate detection of the guide-wire has become a major challenge. In recent years, many general semantic segmentation models have been widely used for guide-wire segmentation, and some studies have used endpoint detection or multi-part position detection of the guide-wire to reduce the difficulty of segmentation, but ignored the morphological features of the guide-wire. In our work, we propose an end-to-end guide-wire segmentation method that incorporates multiple morphological features into neural network to enhance the morphological features of the guide-wire and improve segmentation accuracy. Specifically, we adopt a Ushaped encoder-decoder framework as the backbone network for the overall feature extraction. We then introduce the Tubular Feature Extraction Module (TFEM), designed for elongated tubular objects to enhance the learning of guide wire tubular features. Next, we introduce the Feature Extension Module (FEM) to improve the combining ability of the extracted tubular features. Finally, we devise a Composite Edge Loss (CEL) that focuses more on edge features to further optimize the model's effectiveness in edge details. Our method achieves better guidewire segmentation results for morphological details, achieving state-of-the-art performance on 3669 acquired multi-class X-ray guide-wire images.