A novel U-Net approach to segment the cardiac chamber in magnetic resonance images with ghost artifacts

Objective: We propose a robust technique for segmenting magnetic resonance images of post-atrial septal occlusion intervention in the cardiac chamber.

Methods: A variant of the U-Net architecture is used to perform atrial segmentation via a deep convolutional neural network, and we compare performance with the Kass snake model. It can be used to determine the surgical success of atrial septal occlusion (ASO) pre- and post- the implantation of the septal occluder, which is based on the volume restoration of the right atria (RA) and left atria (LA).

Results: The method was evaluated on a test dataset containing 550 two-dimensional image slices, outperforming conventional active contouring regarding the Dice similarity coefficient, Jaccard index, and Hausdorff distance, and achieving segmentation in the presence of ghost artifacts that occlude the atrium outline. This problem has been unsolvable using traditional machine learning algorithm pertaining to active contouring via the Kass snake algorithm. Moreover, the proposed technique is closer to manual segmentation than the snakes active contour model in mean of atrial area (M-AA), mean of atrial maximum diameter (M-AMXD), mean atrial minimum diameter (M-AMID), and mean angle of the atrial long axis (M-AALA).

Conclusion: After segmentation, we compute the volume ratio of right to left atria, obtaining a smaller ratio that indicates better restoration. Hence, the proposed technique allows to evaluate the surgical success of atrial septal occlusion and may support diagnosis regarding the accurate evaluation of atrial septal defects before and after occlusion procedures.