AI 中文总结
针对医学超声图像分割难题，提出风险路由隐式边界细化框架RIBR，结合多种手段细化轮廓抑制振荡，在九个数据集评估中，于紧凑参数预算下取得最佳总体宏平均并减少边界误差。
AI 中文摘要
医学超声（US）图像分割面临诸多挑战，如斑点噪声、低对比度边界等。尽管基于编码器 - 解码器和变压器的网络有不错表现，但许多方法在外部分布变化时仍存在问题。本文提出风险路由隐式边界细化（RIBR）框架，它将隐式神经表示用作风险路由残差校正。通过结合边界细化隐式残差、风险路由残差控制等手段细化不确定轮廓并抑制非边界振荡。在九个US数据集上评估表明，RIBR在紧凑参数预算下取得最佳总体宏平均，持续减少边界误差。
英文摘要
Medical ultrasound (US) image segmentation faces significant challenges due to speckle noise, low-contrast boundaries, acoustic shadowing, and acquisition variation across operators and clinical centers. Although encoder-decoder and transformer-based networks have achieved strong performance, many methods recover boundary details through dense decoders or larger backbones, which may still produce over-smoothed contours or unstable predictions under external distribution shifts. In this article, we propose Risk-routed Implicit Boundary Refinement (RIBR), a compact segmentation framework that uses implicit neural representation as a risk-routed residual correction rather than an unconstrained full-mask predictor. RIBR combines boundary-refinement implicit residuals, risk-routed residual control, and geometry- and speckle-aware boundary regularization to refine uncertain contours while suppressing non-boundary oscillations. Evaluation on nine US datasets covering lymph nodes, breast lesions, thyroid nodules, and prostate shows that RIBR achieves the best overall macro-average and consistently reduces boundary error across grouped and organ-specific comparisons under a compact parameter budget. These findings suggest that controlled implicit residual learning is a practical strategy for resource-constrained and boundary-sensitive US segmentation. Source code is available at https://github.com/jinggqu/ribr.
