Abstract #4725

Rapid virtually automated technique for renal corticomedullary segmentation from volumetric arterial phase imaging: Initial experience

Kane Nicholls¹, Julia Williams¹, Lucy McKenna², Julie Smith², Emma Hornsey², Elif Ekinci², Leonid Churilov³, Henry Rusinek⁴, Artem Mikheev⁵, and Ruth P Lim¹

¹Radiology, Austin Health, Heidelberg, Australia, ²Austin Health, Heidelberg, Australia, ³Florey Institute of Neuroscience and Mental Health, Heidelberg, Australia, ⁴Radiology, New York University, New York, NY, United States, ⁵New York University, New York, NY, United States

Efficient, reproducible and accurate corticomedullary renal segmentation is challenging but important for MR renography and disease monitoring. We assessed segmentation time, reproducibility and accuracy of a virtually automated (VA) approach (<5 second user interaction), compared to gold standard (GS) manual segmentation. Segmentation time per subject (n=11) was 78.6±7.0s for VA and 60-120min for GS. VA intra- and inter-rater agreement was near perfect for cortex, medullary and whole kidney segmentation (concordance correlation coefficient all ≥0.99), with excellent concordance with GS segmentation (CCC all >0.80). VA is a rapid, accurate and highly reproducible corticomedullary segmentation tool which has promising clinical potential.

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