The recently introduced Segment-Anything Model (SAM) has the potential to greatly accelerate the development of segmentation models. However, directly applying SAM to surgical images has key limitations including (1) the requirement of image-specific prompts at test-time, thereby preventing fully automated segmentation, and (2) ineffectiveness due to substantial domain gap between natural and surgical images. In this work, we propose CycleSAM, an approach for one-shot surgical scene segmentation that uses the training image-mask pair at test-time to automatically identify points in the test images that correspond to each object class, which can then be used to prompt SAM to produce object masks. To produce high-fidelity matches, we introduce a novel spatial cycle-consistency constraint that enforces point proposals in the test image to rematch to points within the object foreground region in the training image. Then, to address the domain gap, rather than directly using the visual features from SAM, we employ a ResNet50 encoder pretrained on surgical images in a self-supervised fashion, thereby maintaining high label-efficiency. We evaluate CycleSAM for one-shot segmentation on two diverse surgical semantic segmentation datasets, comprehensively outperforming baseline approaches and reaching up to 50% of fully-supervised performance.

CycleSAM是一种用于一次性手术场景分割的方法，利用测试时的训练图像-掩码对自动识别与每个对象类别对应的测试图像中的点，并用于提示Segment-Anything Model生成对象掩码。通过引入新的空间循环一致性约束来产生高保真度的匹配，并通过在自监督学习中使用在手术图像上预训练的ResNet50编码器来解决领域差异，从而保持高标记效率。在两个不同的手术语义分割数据集上评估CycleSAM的一次性分割，结果全面优于基准方法，达到了完全监督性能的50%。

CycleSAM: 使用循环一致性特征匹配推动SAM的单次手术场景分割