Segmentation quality assessment (SQA) plays a critical role in the deployment of a medical image based AI system. Users need to be informed/alerted whenever an AI system generates unreliable/incorrect predictions. With the introduction of the Segment Anything Model (SAM), a general foundation segmentation model, new research opportunities emerged in how one can utilize SAM for medical image segmentation. In this paper, we propose a novel SQA method, called SQA-SAM, which exploits SAM to enhance the accuracy of quality assessment for medical image segmentation. When a medical image segmentation model (MedSeg) produces predictions for a test image, we generate visual prompts based on the predictions, and SAM is utilized to generate segmentation maps corresponding to the visual prompts. How well MedSeg's segmentation aligns with SAM's segmentation indicates how well MedSeg's segmentation aligns with the general perception of objectness and image region partition. We develop a score measure for such alignment. In experiments, we find that the generated scores exhibit moderate to strong positive correlation (in Pearson correlation and Spearman correlation) with Dice coefficient scores reflecting the true segmentation quality.

翻译：分割质量评估（SQA）在基于医学图像的人工智能系统部署中起着关键作用。当AI系统生成不可靠/不正确的预测时，用户需要得到通知/警示。随着通用基础分割模型Segment Anything模型（SAM）的引入，为如何利用SAM进行医学图像分割开辟了新的研究机遇。本文提出了一种名为SQA-SAM的新型分割质量评估方法，该方法利用SAM提升医学图像分割质量评估的准确性。当医学图像分割模型（MedSeg）对测试图像生成预测结果时，我们基于这些预测生成视觉提示，并利用SAM生成对应分割图。MedSeg分割结果与SAM分割结果的一致程度，反映了MedSeg分割结果与通用目标感知及图像区域划分的一致性。我们针对这种一致性开发了评分度量。实验表明，生成的评分与反映真实分割质量的Dice系数评分呈中等至强正相关（基于皮尔逊相关系数和斯皮尔曼相关系数）。