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The objective of this article was developing an automated tool for routineclinical practice to estimate urinary stone composition from CT images based onthe density of all constituent voxels. A total of 118 stones for which thecomposition had been determined by infrared spectroscopy were placed in ahelical CT scanner. A standard acquisition, low-dose and high-dose acquisitionswere performed. All voxels constituting each stone were automatically selected.A dissimilarity index evaluating variations of density around each voxel wascreated in order to minimize partial volume effects: stone composition wasestablished on the basis of voxel density of homogeneous zones. Stonecomposition was determined in 52% of cases. Sensitivities for each compoundwere: uric acid: 65%, struvite: 19%, cystine: 78%, carbapatite: 33.5%, calciumoxalate dihydrate: 57%, calcium oxalate monohydrate: 66.5%, brushite: 75%.Low-dose acquisition did not lower the performances (P < 0.05). This entirelyautomated approach eliminates manual intervention on the images by theradiologist while providing identical performances including for low-doseprotocols.

Estimation of urinary stone composition by automated processing of CT images