Myocardium Segmentation
3 papers with code • 1 benchmarks • 0 datasets
Most implemented papers
Factorised spatial representation learning: application in semi-supervised myocardial segmentation
Specifically, we achieve comparable performance to fully supervised networks using a fraction of labelled images in experiments on ACDC and a dataset from Edinburgh Imaging Facility QMRI.
MvMM-RegNet: A new image registration framework based on multivariate mixture model and neural network estimation
Current deep-learning-based registration algorithms often exploit intensity-based similarity measures as the loss function, where dense correspondence between a pair of moving and fixed images is optimized through backpropagation during training.
TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations
mwyburd/TEDS-Net
•
•
We tested our method on myocardium segmentation from an open-source 2D heart dataset.
