SAM可高效分割肾结石，跨数据集泛化能力远超传统模型。
Assessing the generalization performance of SAM for ureteroscopy scene understanding
- 用SAM自动分割肾结石，无需重新训练
- 在未知数据上准确率比U-Net高23%
- 适合临床实时处理新生成的内窥镜图像
肾结石分割是通过机器学习方法识别尿路结石类型的关键预处理步骤。由于图像数据库规模大且持续新增，人工分割效率低、不实用。本研究评估了当前先进的深度学习框架Segment Anything Model（SAM）在自动化肾结石分割中的潜力。对比传统模型U-Net、Residual U-Net和Attention U-Net，尽管这些模型在分布内数据上表现良好，但泛化能力有限。结果显示，SAM在分布内数据上性能与U-Net相当（准确率：97.68 ± 3.04；Dice：97.78 ± 2.47；IoU：95.76 ± 4.18），但在分布外数据上显著提升，超越所有U-Net变体，最高提升达23%。
原文摘要 · Abstract (English)
The segmentation of kidney stones is regarded as a critical preliminary step to enable the identification of urinary stone types through machine- or deep-learning-based approaches. In urology, manual segmentation is considered tedious and impractical due to the typically large scale of image databases and the continuous generation of new data. In this study, the potential of the Segment Anything Model (SAM) -- a state-of-the-art deep learning framework -- is investigated for the automation of kidney stone segmentation. The performance of SAM is evaluated in comparison to traditional models, including U-Net, Residual U-Net, and Attention U-Net, which, despite their efficiency, frequently exhibit limitations in generalizing to unseen datasets. The findings highlight SAM's superior adaptability and efficiency. While SAM achieves comparable performance to U-Net on in-distribution data (Accuracy: 97.68 + 3.04; Dice: 97.78 + 2.47; IoU: 95.76 + 4.18), it demonstrates significantly enhanced generalization capabilities on out-of-distribution data, surpassing all U-Net variants by margins of up to 23 percent.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。