发表机构
University of Kentucky（肯塔基大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对医学成像中目标分割模糊问题，提出SARFA框架，通过概率性提示生成掩码，基于弗雷歇放射组学距离和直接偏好优化进行训练，在CT和MRI基准测试中优于现有方法，有效提升医学图像分割效果。
AI 中文摘要
分割一切模型（SAM）在各种分割任务中展现出强大的通用性。然而，在待分割目标模糊的情况下，SAM往往表现不佳。这在医学成像中是个问题，准确勾勒肿瘤等目标至关重要，但即使是专家放射科医生也可能在目标的合适边界上存在分歧。为解决此问题，我们提出了SARFA（基于放射组学特征对齐的任意分割），这是一个用于改进医学图像分割的新框架。通过概率性提示，SARFA为每个输入图像生成一组多样的合理掩码，并基于弗雷歇放射组学距离（FRD）和直接偏好优化（DPO），用放射组学驱动的训练目标对其进行优化。通过最小化每个图像中掩码预测区域与真实区域之间的FRD，SARFA鼓励分割输出的解剖和纹理特征与具有临床意义的真实表示对齐，而不仅仅依赖像素级重叠。在计算机断层扫描（CT）和磁共振成像（MRI）基准上进行评估，SARFA优于现有的模糊分割方法，证明了放射组学特征对齐和DPO式候选掩码排序作为训练目标的有效性。我们的代码可在这个https网址获取。
英文摘要
The Segment Anything Model (SAM) has demonstrated strong generalizability across a variety of segmentation tasks. However, SAM often struggles in situations where the target to be segmented is ambiguous. This poses a problem in medical imaging, where accurate delineation of targets such as tumors is vital, but even expert radiologists can disagree on the appropriate boundary for a target. Addressing this, we propose SARFA (Segment Anything with Radiomic Feature Alignment), a novel framework for improved medical image segmentation. Via probabilistic prompting, SARFA generates a diverse set of plausible masks for each input image and optimizes them with a radiomics-driven training objective based on Fréchet Radiomic Distance (FRD) and Direct Preference Optimization (DPO). By minimizing the FRD between masked predicted and ground truth regions within each image, SARFA encourages segmentation outputs whose anatomical and textural characteristics align with clinically meaningful ground truth representations, without relying solely on pixel-level overlap. Evaluated on computed tomography (CT) and magnetic resonance imaging (MRI) benchmarks, SARFA outperforms existing ambiguous segmentation methods, demonstrating the effectiveness of radiomic feature alignment and DPO-style candidate mask ranking as a training objective. Our code is available at https://github.com/tbwa233/SARFA.
Comments15 pages, 9 figures, 5 tables, 1 algorithm
