Meeting Banner
Abstract #3503

Fully Convolutional Networks for Automated Segmentation of Abdominal Adipose Tissue Depots in Water-Fat MRI

Taro Langner1, Anders Hedström2, Håkan Ahlström1,2, and Joel Kullberg1,2

1Department of Radiology, Uppsala University, Uppsala, Sweden, 2BioVenture Hub, Antaros Medical, Mölndal, Sweden

The segmentation and quantification of human adipose tissue depots offers new insights into the development of metabolic and cardiovascular disease but is often hindered by the need for time-consuming and subjective manual input. We propose an automatic method that uses a convolutional neural network for the segmentation of both visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). The network was applied to two-dimensional slices of 90 water-fat MRI scans of the abdomen. In a 10-fold cross-validation it reached average dice scores of 0.979 (VAT) and 0.987 (SAT), with average absolute quantification errors of 0.8% (VAT) and 0.5% (SAT).

This abstract and the presentation materials are available to members only; a login is required.

Join Here