Cardiac Segmentation: A Comparative Study Between 3D UNet and 2D UNet performances

Abstract

Background: the process of cardiac segmentation using cardiac MRI images has been widely studied, and various deep learning models have been employed to address the complexities of heart chamber segmentation. Among these models, the 2-Dimensional (2D) UNet has demonstrated good performance in segmenting the left and right ventricles but has not been utilized to differential between the myocardium and papillary muscles. Consequently, researchers have proposed the use of the 3-Dimensional (3D) UNet as an alternative to the 2D UNet to improve segmentation outcomes. This study aims to compare the accuracy of 2D and 3D UNet models in segmenting the left ventricle using MRI images. Method: Both models were trained and tested on public ACDC dataset including 150 patients. Both models were trained for 140 epochs. To compare the accuracy of 2D and 3D UNet models, Dice Score Coefficient (DSC) and Hausdorff Distance (HD) were computed. Results: The 2D model achieved a mean Dice of 0.851 and a mean HD of 4.31 mm while the 3D UNet model achieved a higher performance in comparison with the 2D model with a mean Dice of 0.950 and a mean HD of 3.14 mm. Conclusion: The outcome of this study showed that 3D UNet is more suitable for the cardiac MRI segmentation.