Abstract #3677

Automated MR HIC Determination using Deep Learning and Frangi Filters

Ralf Berthold Loeffler^1,2, M. Beth McCarville², Aaryani Tipirneni-Sajja^2,3, Jane S Hankins⁴, and Claudia Maria Hillenbrand^1,2

¹Research Imaging NSW, University of New South Wales, Sydney, Australia, ²Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN, United States, ³Biomedical Engineering, University of Memphis, Memphis, TN, United States, ⁴Hematology, St. Jude Children's Research Hospital, Memphis, TN, United States

Hepatic iron content (HIC) quantification requires segmentation. Deep learning and Frangi Filtering allow to fully automate segmentation. 664 manually segmented data sets were available for training and testing a UNET. Data sets segmented by UNET were Frangi filtered for vessel exclusion, HIC was calculated using a published calibration, and correlated with HIC obtained with manual segmentation. Very good correlation (R² > 0.99) with a correlation line close to unity was found. Fully automated HIC quantification using deep learning and Frangi filtering can lead to significant time savings in clinical practice.

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