Abstract
Accurate semantic segmentation of foot ulcers is essential for automated wound monitoring, yet boundary delineation remains challenging due to tissue heterogeneity and poor contrast with surrounding skin. To overcome the limitations of standard intensity-based networks, we present LSS-LTCNet:an ante-hoc explainable framework synergizing deterministic structural priors with continuous-time neural dynamics. Our architecture departs from traditional black-box models by employing a Local Self-Similarity (LSS) mechanism that extracts dense, illumination-invariant texture descriptors to explicitly disentangle necrotic tissue from background artifacts. To enforce topological precision, we introduce a Liquid Time-Constant (LTC) refinement module that treats boundary evolution as an ODEgoverned dynamic system, iteratively refining masks over continuous time-steps. Comprehensive evaluation on the MICCAI FUSeg dataset demonstrates that LSS-LTCNet achieves state-of-the-art boundary alignment, securing a peak Dice score of 86.96% and an exceptional 95th percentile Hausdorff Distance (HD95) of 8.91 pixels. Requiring merely 25.70M parameters, the model significantly outperforms heavier U-Net and transformer baselines in efficiency. By providing inherent visual audit trails alongside high-fidelity predictions, LSS-LTCNet offers a robust and transparent solution for computer-aided diagnosis in mobile healthcare (mHealth) settings.
Abstract (translated)
足部溃疡的精确语义分割对于自动化伤口监测至关重要，但由于组织异质性和与周围皮肤对比度差，边界划分仍然具有挑战性。为了克服基于强度的标准网络的局限性，我们提出了LSS-LTCNet：一种事前可解释框架，该框架结合了确定性的结构先验和连续时间神经动力学。我们的架构通过采用局部自相似（Local Self-Similarity, LSS）机制来提取密集且不受光照影响的纹理描述符而不同于传统的黑盒模型，这种机制能够明确区分坏死组织与背景伪影。为了确保拓扑精度，我们引入了一个液态时间常数（Liquid Time-Constant, LTC）细化模块，该模块将边界演化视为一个受ODE（常微分方程）支配的动力系统，并在连续的时间步长中迭代地优化掩模。 在MICCAI FUSeg数据集上的全面评估表明，LSS-LTCNet实现了最先进的边界对齐效果，达到了86.96%的峰值Dice分数和出色的第95百分位Hausdorff距离（HD95）为8.91像素。该模型仅需25.70M参数，在效率上显著优于更重的U-Net和变压器基线。通过提供固有的视觉审核轨迹以及高保真的预测，LSS-LTCNet为移动医疗环境中的计算机辅助诊断提供了可靠且透明的解决方案。
URL
https://arxiv.org/abs/2603.00459
