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Abstract #1725

Deep Learning Model for Liver MRI Segmentation

Amber Michele Mittendorf1, Lawrence Ngo1, Erol Bozdogan1, Mohammad Chaudhry1, Steven Chen1, Gemini Janas1,2, Jacob Johnson3, Zhe Zhu1, Maciej Mazurowski1, and Mustafa R Bashir1,2,4

1Department of Radiology, Duke University Medical Center, Durham, NC, United States, 2Center for Advanced Magnetic Resonance Imaging, Duke University Medical Center, Durham, NC, United States, 3Department of Radiology, University of Wisconsin, Madison, WI, United States, 4Department of Medicine, Gastroenterology, Duke University Medical Center, Durham, NC, United States

Hepatic segmentation is an important but tedious clinical task used in a variety of applications. Existing techniques are relatively narrow in scope, requiring a particular type of MRI sequence or CT for accurate segmentation. We developed a Convolutional Neural Network (CNN) capable of automated liver segmentation on single-shot fast spin echo, T1-weighted, or opposed phase proton-density (OP-PD) weighted sequences using separate training/validation and testing data sets. Compared to human segmenters, the CNN performed well, with volumetric DICE coefficients of 0.92-0.95. The CNN performed least consistently on OP-PD sequences, which had the smallest number of cases in the training/validation data set.

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