Leveraging Persistence Image to Enhance Robustness and Performance in Curvilinear Structure Segmentation

2026-01-25

AI Summary

This work addresses the challenge of segmenting curvilinear structures in medical images, which is often hindered by imaging artifacts and the difficulty of preserving topological connectivity. The authors propose Topology SegNet, a novel framework that, for the first time, integrates persistence images—a differentiable and learnable topological representation—directly into the U-Net backbone. By fusing topological information during both encoding and decoding stages, the method eliminates the need for handcrafted topological loss functions. Evaluated on three benchmark datasets for curvilinear structure segmentation, Topology SegNet achieves state-of-the-art performance in both pixel-level accuracy and topological fidelity, demonstrating significantly enhanced robustness and generalization under image degradations such as overexposure and blur.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Structured Learning
• Search and Optimization: Non-convex Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Segmenting curvilinear structures in medical images is essential for analyzing morphological patterns in clinical applications. Integrating topological properties, such as connectivity, improves segmentation accuracy and consistency. However, extracting and embedding such properties - especially from Persistence Diagrams (PD) - is challenging due to their non-differentiability and computational cost. Existing approaches mostly encode topology through handcrafted loss functions, which generalize poorly across tasks. In this paper, we propose PIs-Regressor, a simple yet effective module that learns persistence image (PI) - finite, differentiable representations of topological features - directly from data. Together with Topology SegNet, which fuses these features in both downsampling and upsampling stages, our framework integrates topology into the network architecture itself rather than auxiliary losses. Unlike existing methods that depend heavily on handcrafted loss functions, our approach directly incorporates topological information into the network structure, leading to more robust segmentation. Our design is flexible and can be seamlessly combined with other topology-based methods to further enhance segmentation performance. Experimental results show that integrating topological features enhances model robustness, effectively handling challenges like overexposure and blurring in medical imaging. Our approach on three curvilinear benchmarks demonstrate state-of-the-art performance in both pixel-level accuracy and topological fidelity.

Problem

• curvilinear structure segmentation
• topological features
• Persistence Diagrams
• medical image analysis
• segmentation robustness

Innovation

• Persistence Image
• Topological Feature Integration
• Curvilinear Structure Segmentation
• Differentiable Topology Representation
• Topology-aware Neural Architecture