3D Consistent Biventricular Myocardial Segmentation Using Deep Learning for Mesh Generation
This work addresses the need for reliable myocardial segmentation in medical imaging, which is incremental as it builds on existing deep learning methods with specific adaptations for consistency and small datasets.
The authors tackled the problem of automated 3D-consistent segmentation of both left and right ventricular myocardium in MRI volumes for mesh generation, achieving accurate and consistent results validated on 15 cases from STACOM with 5-fold cross-validation.
We present a novel automated method to segment the myocardium of both left and right ventricles in MRI volumes. The segmentation is consistent in 3D across the slices such that it can be directly used for mesh generation. Two specific neural networks with multi-scale coarse-to-fine prediction structure are proposed to cope with the small training dataset and trained using an original loss function. The former segments a slice in the middle of the volume. Then the latter iteratively propagates the slice segmentations towards the base and the apex, in a spatially consistent way. We perform 5-fold cross-validation on the 15 cases from STACOM to validate the method. For training, we use real cases and their synthetic variants generated by combining motion simulation and image synthesis. Accurate and consistent testing results are obtained.