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Image segmentation has been increasingly applied in medical settings asrecent developments have skyrocketed the potential applications of deeplearning. Urology, specifically, is one field of medicine that is primed forthe adoption of a real-time image segmentation system with the long-term aim ofautomating endoscopic stone treatment. In this project, we explored superviseddeep learning models to annotate kidney stones in surgical endoscopic videofeeds. In this paper, we describe how we built a dataset from the raw videosand how we developed a pipeline to automate as much of the process as possible.For the segmentation task, we adapted and analyzed three baseline deep learningmodels -- U-Net, U-Net++, and DenseNet -- to predict annotations on the framesof the endoscopic videos with the highest accuracy above 90\%. To show clinicalpotential for real-time use, we also confirmed that our best trained model canaccurately annotate new videos at 30 frames per second. Our results demonstratethat the proposed method justifies continued development and study of imagesegmentation to annotate ureteroscopic video feeds.

Segmentation of kidney stones in endoscopic video feeds