SARFA: Segment Anything with Radiomic Feature Alignment Improves Medical Image Segmentation

Jul 16, 2026

Researchers introduce SARFA, a framework that enhances the Segment Anything Model (SAM) for medical image segmentation by incorporating probabilistic prompting and a radiomics-driven training objective. SARFA aligns anatomical and textural features of predicted segmentations with clinical ground truth using Fréchet Radiomic Distance and Direct Preference Optimization. The method demonstrates improved performance over existing ambiguous segmentation approaches on CT and MRI benchmarks.

Why it matters: This work offers a novel approach to addressing ambiguous boundaries in medical imaging, which could improve the accuracy of tumor delineation and support better clinical decision-making.

Full story at: arXiv Computer Vision ↗