Triple-Path Kolmogorov–Arnold Networks with Deep Mutual Learning for Robust Retinal Microvascular Segmentation

Abstract

Precise segmentation of the retinal microvasculature is vital for early detection and longitudinal monitoring of systemic cardiovascular and ophthalmic diseases. However, contemporary deep learning models struggle to simultaneously preserve fine-vessel branches and suppress background noise, often leading to severe under-segmentation or false-positive artifacts. To address this persistent challenge, we propose a novel, highly efficient Triple-Path Kolmogorov–Arnold Network (KAN) optimized via Deep Mutual Learning (DML). First, the proposed architecture synergistically integrates U-Net, HaarNet, and SegNet to explicitly decouple global contextual feature extraction from high-frequency spatial edge detection. Second, we introduce field-of-view spatial constraints to strictly eliminate background interference. Third, traditional linear bottlenecks are replaced with KAN blocks, which leverage dynamic, Chebyshev polynomial-based activation functions to robustly model highly nonlinear, chaotic vascular topologies. Finally, a zero-cost DML strategy is employed during training, maximizing the network’s representational capacity without introducing any computational burden during inference. Extensive evaluations demonstrate that our proposed model successfully overcomes the traditional sensitivity-specificity trade-off. Specifically, it achieves a peak sensitivity of 72.38%, a specificity of 98.66%, and an overall Dice score of 72.48%. While maintaining exceptional computational efficiency, this tightly coupled framework establishes a robust, accurate, and clinically viable solution for automated point-of-care retinal diagnostics.