Kidney Stone Detection and Segmentation Using a YOLO v11 + UNet Pipeline

Kidney stones, or renal calculi, affect approximately 10% of the global population and can lead to severe complications such as urinary obstruction and kidney failure. We propose a two-stage pipeline for automated detection and segmentation of kidney stones in CT images. The first stage uses a YOLOv11 network trained on a public dataset to identify regions of interest (ROIs). High-quality segmentation masks, generated using the Segment Anything Model (SAM), were added to the same dataset and used to train a U-Net for detailed segmentation. The final pipeline combines YOLO for ROI detection and U-Net for segmentation, achieving strong performance with metrics such as a Dice coefficient of 0.93 and IoU of 0.88. This approach contributes to kidney stone research and provides a scalable framework for nephrolithiasis assessment in clinical applications.