Cardiac Image Segmentation Under Respiratory Motion Artifacts Using Different Modified U-Net Models

Precise segmentation of cardiac magnetic resonance images (CMR) is essential to quantitative measurements of the heart, yet it is often affected by respiratory motion misregistration's and this is particularly common in free-breathing patient environments. These artifacts reduce the quality of the image and present a big challenge to traditional deep learning models such as the baseline U-Net. This paper carries out a systematic evaluation of the capabilities of four alternate U-Net architectures, Attention U-Net, Dense U-Net, Residual U-Net and Sharp U-Net, on CMR data with synthetically added motion artifacts of various intensities. To add synthetic artifacts, the deformation fields were employed that mimic the realistic breathing-induced deformations and obtained the clean, mild, moderate, and severe moving conditions. Hybrid loss function that combines Dice, Focal, and Tversky losses was used to train each model to create robustness to class imbalance and boundary degradation. The segmentation quality was assessed by the following evaluation metrics: Dice Similarity Coefficient (DSC), Hausdorff Distance (HD) and Jaccard Index (JI). Their results show that Sharp U-Net had the best overall performance with the highest average DSC (0.927) and lowest HD (3.62 mm) in severe motion conditions. The region centered attention mechanisms were also capable of withstanding these in Attention U-Net. The dense and Residual U-Nets did not perform much better than the baseline, and did not perform as well when there were severe artifacts. These results show the significance of architectural improvements and motion consciousness instruction strategies in guaranteeing the dependable segmentation. The article provides a scalable and reproducible methodology to assess the performance of motion-robust deep learning models for CMR and adaptable for use in other clinical diagnostic applications where respiratory gating is not available. The proposed framework provides a reproducible and computationally practical approach to motion-robust cardiac MRI segmentation. Sharp U-Net achieved the highest segmentation performance under severe respiratory motion, with a DSC of 0.784, compared with 0.778 for Attention U-Net, while requiring a slightly higher inference latency of approximately 28-30 ms per 256 × 256 slice and GPU memory consumption of approximately 1.8 GB. This modest computational overhead represents a trade-off between improved boundary preservation and computational efficiency and should be considered when deploying the model in resource-constrained clinical environments. Across the clean, mild, moderate, and severe respiratory motion conditions, Sharp U-Net achieved an average DSC of 0.927. Under the most challenging severe-motion condition, the DSC decreased to 0.784, while maintaining an ASSD of 1.69 mm, sensitivity of 0.823, precision of 0.774, and RVE of 8.9%.