Towards Automatic Recognition of Pure Mixed Stones using Intraoperative Endoscopic Digital Images
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Objective: To assess automatic computer-aided in-situ recognition of morphological features of pure and mixed urinary stones using intraoperative digital endoscopic images acquired in a clinical setting. Materials and methods: In this single-centre study, an experienced urologist intraoperatively and prospectively examined the surface and section of all kidney stones encountered. Calcium oxalate monohydrate (COM/Ia), dihydrate (COD/IIb) and uric acid (UA/IIIb) morphological criteria were collected and classified to generate annotated datasets. A deep convolutional neural network (CNN) was trained to predict the composition of both pure and mixed stones. To explain the predictions of the deep neural network model, coarse localisation heat-maps were plotted to pinpoint key areas identified by the network. Results: This study included 347 and 236 observations of stone surface and stone section, respectively. A highest sensitivity of 98 IIIb/UA" using surface images. The most frequently encountered morphology was that of the type "pure Ia/COM"; it was correctly predicted in 91 cases using surface and section images, respectively. Of the mixed type "Ia/COM+IIb/COD", Ia/COM was predicted in 84 IIb/COD in 70 Ia/COM+IIIb/UA stones, Ia/COM was predicted in 91 images, IIIb/UA in 69 preliminary study demonstrates that deep convolutional neural networks are promising to identify kidney stone composition from endoscopic images acquired intraoperatively. Both pure and mixed stone composition could be discriminated. Collected in a clinical setting, surface and section images analysed by deep CNN provide valuable information about stone morphology for computer-aided diagnosis.
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