Deep Topology-Preserving Network for Skeleton Extraction and Node Identification of Tight Junctions in Retinal Pigment Epithelium Images

The structural integrity of tight junction (TJ) networks in the retinal pigment epithelium is a key indicator of the function of the outer blood-retinal barrier (oBRB). However, traditional automatic segmentation methods often suffer from topological discontinuities, resulting in fragmented predictions that fail to accurately reflect the barrier’s state. In this study, we propose a topology-preserving deep learning framework specifically designed for TJ skeleton extraction and node identification. Our method employs a multi-task bidirectional architecture that simultaneously models both the midline structure and connecting nodes, and incorporates a composite loss function (clDice) constrained by soft skeleton similarity to explicitly enforce global structural connectivity. Quantitative evaluations indicate that the proposed method significantly improves topological consistency, with a Betti error of 3.3182, a Graph Connectivity Ratio (GCR) of 0.6858, and a Mean Node Degree Error (MNDE) of 1.6977. Although the F1 score for connectivity is 0.7830, the predicted network outperforms the standard model in terms of morphological fidelity and connectivity. These findings underscore the necessity of adopting topology-aware modeling in the process of biological network extraction, providing a solid computational foundation for the objective quantitative analysis of morphological stability in tightly connected networks in both clinical and experimental research.