Comparison of two-dimensional and three-dimensional U-Net architectures for segmentation of adipose tissue in cardiac magnetic resonance images.

The process of identifying cardiac adipose tissue (CAT) from volumetric magnetic resonance imaging of the heart is tedious, time-consuming, and often dependent on observer interpretation. Many 2-dimensional (2D) convolutional neural networks (CNNs) have been implemented to automate the cardiac segmentation process, but none have attempted to identify CAT. Furthermore, the results from automatic segmentation of other cardiac structures leave room for improvement. This study investigated the viability of a 3-dimensional (3D) CNN in comparison to a similar 2D CNN. Both models used a U-Net architecture to simultaneously classify CAT, left myocardium, left ventricle, and right myocardium. The multi-phase model trained with multiple observers' segmentations reached a whole-volume Dice similarity coefficient (DSC) of 0.925 across all classes and 0.640 for CAT specifically; the corresponding 2D model's DSC across all classes was 0.902 and 0.590 for CAT specifically. This 3D model also achieved a higher level of CAT-specific DSC agreement with a group of observers with a Williams Index score of 0.973 in comparison to the 2D model's score of 0.822.