Topology-preserving segmentation of abnormal nailfold capillaries using dynamic snake convolution and Transformer-augmented U 2 -Net

Objective: Nailfold capillary morphology provides clinically essential information for the noninvasive assessment of autoimmune, metabolic, and cardiovascular disorders. However, accurate segmentation of abnormal capillaries is highly challenging for conventional convolutional networks due to their thin, tortuous, and intertwined structures, as well as weak contrast against complex backgrounds. To address these limitations, we aimed to develop a novel network architecture explicitly designed for robust abnormal nailfold capillary segmentation.Approach:We developed TDS-U 2 Net, a novel variant of the U 2 -Net architecture. The proposed framework integrates dynamic snake convolution (DSConv) to enhance sensitivity to slender and curved local geometries, and incorporates a Transformer module to strengthen long-range dependency modeling and multiscale contextual fusion. Furthermore, a topological continuity constraint loss (TCLoss) function is employed alongside cross-entropy loss to penalize structural discontinuities and reduce breaks in thin tubular predictions.Main results: Evaluated on a curated nailfold capillary dataset, TDS-U 2 Net outperformed existing methods, achieving an accuracy of 98.33%, a Dice coefficient of 88.56%, an Intersection over Union of 80.92%, an F1-score of 88.23%, and the lowest Hausdorff distance of 6.98 pixels. Comprehensive ablation experiments confirmed that the inclusion of DSConv, the Transformer module, and TCLoss each contributed complementary performance improvements.Significance: By significantly improving structural continuity and preserving fine morphological details, the TDS-U 2 Net framework successfully overcomes the limitations of conventional segmentation networks. This approach provides a highly robust basis for downstream quantitative nailfold capillaroscopy analyses, facilitating the reliable extraction of clinical biomarkers such as vessel length, width, and density.