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Abstract #0554

An End-to-End Segmentation Pipeline for Dixon Adipose and Muscle by Neural Nets (DAMNN)

Karl Landheer1, Jonathan Marchini1, Benjamin Geraghty1, Prodromos Parasoglou1, Stefanie Hectors1, Farshid Sepehrband1, Nicholas Gale1, Andrew Murphy1, Johnathon Walls1, and Mary Germino1
1Regeneron Pharmaceuticals, Inc, Tarrytown, NY, United States


An end-to-end pipeline was developed that processes whole-body Dixon MRI data sets from UK Biobank and corrects for overlapping slices, inhomogeneous signal intensities, and fat-water swaps to produce high quality 3D data sets. Segmentation maps for subcutaneous/visceral fat and left/right thigh muscles from these 3D data sets were then produced using neural networks, and muscle and fat volume phenotypes were extracted. The Jaccard index for the validation data sets was 93.3% for the fat segmentation and 96.9% for the muscle segmentation. Excellent correspondence was obtained with the extracted muscle and fat volumes and similar metrics from a commercially available package.

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