Assessing the generalization performance of SAM for ureteroscopy scene understanding
This addresses the tedious manual segmentation problem for urologists, but it is incremental as it applies an existing method to a new medical domain.
The study tackled kidney stone segmentation in ureteroscopy by evaluating the Segment Anything Model (SAM), finding that SAM achieved comparable performance to U-Net on in-distribution data (e.g., Accuracy: 97.68 + 3.04) and significantly better generalization on out-of-distribution data, surpassing U-Net variants by up to 23 percent.
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.