{"slug": "risk-routed-implicit-boundary-refinement-for-robust-ultrasound-image", "title": "Risk-Routed Implicit Boundary Refinement for Robust Ultrasound Image Segmentation", "summary": "Researchers propose Risk-routed Implicit Boundary Refinement (RIBR), a compact segmentation framework that uses implicit neural representation as a risk-routed residual correction for robust ultrasound image segmentation. Evaluation on nine ultrasound datasets covering lymph nodes, breast lesions, thyroid nodules, and prostate shows RIBR achieves the best overall macro-average and consistently reduces boundary error under a compact parameter budget. Source code is available at https://github.com/jinggqu/ribr.", "body_md": "arXiv:2607.21787v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/risk-routed-implicit-boundary-refinement-for-robust-ultrasound-image", "canonical_source": "https://arxiv.org/abs/2607.21787", "published_at": "2026-07-27 04:00:00+00:00", "updated_at": "2026-07-27 04:27:20.376837+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "computer-vision"], "entities": ["Risk-routed Implicit Boundary Refinement", "RIBR", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/risk-routed-implicit-boundary-refinement-for-robust-ultrasound-image", "markdown": "https://wpnews.pro/news/risk-routed-implicit-boundary-refinement-for-robust-ultrasound-image.md", "text": "https://wpnews.pro/news/risk-routed-implicit-boundary-refinement-for-robust-ultrasound-image.txt", "jsonld": "https://wpnews.pro/news/risk-routed-implicit-boundary-refinement-for-robust-ultrasound-image.jsonld"}}