Cardiac MRI segmentation using shifted-window multilayer perceptron mixer networks.

In this work, we proposed a deep-learning segmentation algorithm for cardiac magnetic resonance imaging (MRI) to aid in contouring of the left ventricle (LV), right ventricle (RV), and Myocardium (Myo). 
Methods: We proposed a shifted window multilayer perceptron (Swin-MLP) mixer network which is built upon a 3D U-shaped symmetric encoder-decoder structure. We evaluated our proposed network using public data from 100 individuals. The network performance was quantitatively evaluated using 3D volume similarity between the ground truth contours and the predictions using Dice score coefficient, sensitivity, and precision as well as 2D surface similarity using Hausdorff distance (HD), mean surface distance (MSD) and residual mean square distance (RMSD). We benchmarked the performance against two other current leading edge networks known as Dynamic UNet and Swin-UNetr on the same public dataset. 
Results: The proposed network achieved the following volume similarity metrics when averaged over three cardiac segments: Dice = 0.952±0.017, precision = 0.948±0.016, sensitivity = 0.956±0.022. The average surface similarities were HD = 1.521±0.121 mm, MSD = 0.266±0.075 mm, and RMSD = 0.668±0.288 mm. The network shows statistically significant improvement in comparison to the Dynamic UNet and Swin-UNetr algorithms for most volumetric and surface metrics with p-value less than 0.05. Overall, the proposed Swin-MLP mixer network demonstrates better or comparable performance than competing methods. 
Conclusions: The proposed Swin-MLP mixer network demonstrates more accurate segmentation performance compared to current leading edge methods. This robust method demonstrates the potential to streamline clinical workflows for multiple applications.