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3D Consistent Biventricular Myocardial Segmentation Using Deep Learning for Mesh Generation

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

By Zheng, Qiao, Delingette, Hervé, Duchateau, Nicolas, Ayache, NicholasResearch paper

Resource information

Authors
Zheng, Qiao, Delingette, Hervé, Duchateau, Nicolas, Ayache, Nicholas
Research area
Artificial intelligence
Research topics
Computer Vision and Pattern Recognition (cs.CV), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences
Publication date
2018
DOI
10.48550/arxiv.1803.11080
Upstream last modified
Mar 2, 2022
License
arXiv.org perpetual, non-exclusive license
Last record update
Nov 4, 2026

About this resource

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.