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

TL;DR

This paper introduces a novel deep learning framework that incorporates differentiable topological features via persistence images to improve the robustness and accuracy of curvilinear structure segmentation in medical images.

Contribution

It proposes PIs-Regressor and Topology SegNet, enabling direct integration of topological information into neural networks without handcrafted loss functions.

Findings

Achieves state-of-the-art accuracy on three benchmarks.

Enhances robustness against overexposure and blurring.

Effectively preserves topological fidelity in segmentation.

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.