Myocardium Segmentation

3 papers with code • 1 benchmarks • 0 datasets

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Most implemented papers

Factorised spatial representation learning: application in semi-supervised myocardial segmentation

agis85/spatial_factorisation 19 Mar 2018

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

xzluo97/MvMM-RegNet 28 Jun 2020

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 28 Jul 2021

We tested our method on myocardium segmentation from an open-source 2D heart dataset.