Fully automated cardiac MRI segmentation using dilated residual network

Purpose: Cardiac ventricle segmentation from cine magnetic resonance imaging (CMRI) is a recognized modality for the noninvasive assessment of cardiovascular pathologies. Deep learning based algorithms achieved state-of-the-art result performance from CMRI cardiac ventricle segmentation. However, most approaches received less attention at the bottom layer of UNet, where main features are lost due to pixel degradation. To increase performance, it is important to handle the bottleneck layer of UNet properly. Considering this problem, we enhanced the performance of main features at the bottom layer of network. Method: We developed a fully automatic pipeline for segmenting the right ventricle (RV), myocardium (MYO), and left ventricle (LV) by incorporating short-axis CMRI sequence images. We propose a dilated residual network (DRN) to capture the features at full resolution in the bottleneck of UNet. Thus, it significantly increases spatial and temporal information and maintains the localization accuracy. A data-augmentation technique is employed to avoid overfitting and class imbalance problems. Finally, output from each expanding path is added pixel-wise to improve the training response. Results: We used and evaluated our proposed method on automatic cardiac diagnosis challenge (ACDC). The test set consists of 50 patient records. The overall dice similarity coefficient (DSC) we achieved for our model is 0.924 ± 0.03, 0.907 ± 0.01, and 0.949 ± 0.05 for RV, MYO, and LV, respectively. Similarly, we obtained hausdorff distance (HD) scores of 10.09 ± 0.01, 7.25 ± 0.05, and 6.86 ± 0.02 mm for RV, MYO, and LV, respectively. The results show superior performance and outperformed state-of-the-art methods in terms of accuracy and reached expert-level segmentation. Consequently, the overall DSC and HD result improved by 1.0% and 1.5%, respectively. Conclusion: We designed a dilated residual UNet (DRN) for cardiac ventricle segmentation using short-axis CMRI. Our method has the advantage of restoring and capturing spatial and temporal information by expanding the receptive field without degrading the image main features in the bottleneck of UNet. Our method is highly accurate and quick, taking 0.28 s on average to process 2D MR images. Also, the network was designed to work on predictions of individual MR images to segment the ventricular region, for which our model outperforms many state-of-the-art methods.