Abstract #1645

Improving Kidney Volume Measurement Reproducibility in ADPKD by Averaging Measurements on Multiple Sequences

Hreedi Dev¹, Chenglin Zhu¹, Arman Sharbatdaran¹, Syed I. Raza¹, Sophie Wang¹, Dominick J. Romano¹, Akshay Goel¹, Kurt Teichman¹, Mina C. Moghadam¹, George Shih¹, Jon D. Blumenfeld^1,2, Daniil Shimonov², James Chevalier², and Martin R. Prince^1,3

¹Radiology, Weill Cornell Medicine, New York, NY, United States, ²Rogosin Institute, New York, NY, United States, ³Radiology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States

Synopsis

Keywords: Kidney, Kidney, MRI, ADPKD

Organ volume measurements on MRI are typically performed on a single pulse sequence because of the tedious process of manual contouring. Here we use deep learning to automate kidney segmentations so that kidney volume can be measured on five abdominal MRI sequences. In 17 subjects scanned twice within 3 weeks (when no change in kidney volume was expected), the power of averaging 5 measurements improved reproducibility, achieving 2.5% absolute percent difference compared to 5.9% with manual contouring (p<0.05). Absolute percent error was reduced further to 2.1%, p<0.05 compared to manual segmentations, by excluding outlier measurements.

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