Kidney stones are a common urologic condition with a high amount of recurrence. Recurrence depends on a multitude of factors the incidence of precursors to kidney stones, plugs, and plaques. One method of characterising the stone precursors is endoscopic assessment, though it is manual and time-consuming. Deep learning has become a popular technique for semantic segmentation because of the high accuracy that has been demonstrated. The present Letter examined the efficacy of deep learning to segment the renal papilla, plaque, and plugs. A U-Net model with ResNet-34 encoder was tested; the Letter examined dropout (to avoid overtraining) and two different loss functions (to address the class imbalance problem. The models were then trained in 1666 images and tested on 185 images. The Jaccard-cross-entropy loss function was more effective than the focal loss function. The model with the dropout rate 0.4 was found to be more effective due to its generalisability. The model was largely successful at delineating the papilla. The model was able to correctly detect the plaques and plugs; however, small plaques were challenging. Deep learning was found to be applicable for segmentation of an endoscopic image for the papilla, plaque, and plug, with room for improvement.
Keywords: Jaccard-cross-entropy loss function; ResNet-34 encoder; U-Net model; automatic detection; calcium compounds; calcium phosphate deposit plugs; class imbalance problem; convolutional neural nets; deep learning; dropout rate; endoscopes; endoscopic assessment; endoscopic image; entropy; focal loss function; image coding; image segmentation; kidney; kidney stones; kidney tubules; learning (artificial intelligence); medical image processing; plaque; renal papilla; semantic segmentation; stone precursors; urologic condition.