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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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