A discontinuity-preserving regularization for deep learning-based cardiac image registration

Objective. Sliding motion may occur between organs in anatomical regions due to respiratory motion and heart beating. This issue is often neglected in previous studies, resulting in poor image registration performance. A new approach is proposed to handle discontinuity at the boundary and improve registration accuracy. Approach. The proposed discontinuity-preserving regularization (DPR) term can maintain local discontinuities. It leverages the segmentation mask to find organ boundaries and then relaxes the displacement field constraints in these boundary regions. A weakly supervised method using mask dissimilarity loss (MDL) is also proposed. It employs a simple formula to calculate the similarity between the fixed image mask and the deformed moving image mask. These two strategies are added to the loss function during network training to guide the model better to update parameters. Furthermore, during inference time, no segmentation mask information is needed. Main results. Adding the proposed DPR term increases the Dice coefficients by 0.005, 0.009, and 0.081 for three existing registration neural networks CRNet, VoxelMorph, and ViT-V-Net, respectively. It also shows significant improvements in other metrics, including Hausdorff Distance and Average Surface Distance. All quantitative indicator results with MDL have been slightly improved within 1%. After applying these two regularization terms, the generated displacement field is more reasonable at the boundary, and the deformed moving image is closer to the fixed image. Significance. This study demonstrates that the proposed regularization terms can effectively handle discontinuities at the boundaries of organs and improve the accuracy of deep learning-based cardiac image registration methods. Besides, they are generic to be extended to other networks.