LAUSR

Non-contrast computed tomography (NCCT) relies on labour-intensive examinations of CT slices to identify urolithiasis in the urinary tract, so several algorithms including deep learning method were proposed for segmenting urinary stones. The most problematic point is that many non-stone objects were detected together. The proposed method is an end-to-end deep learning algorithm which could reduce these false positive effectively, and showed high segmentation performance. Automated segmentation is not only could reduce the detection burden, but also help physicians to determine the treatment strategy more precisely measuring the size, volume, stone-to-skin distance, mean stone density, etc. in 3d-space.