  In this work, we attempt the segmentation of cardiac structures in late
gadolinium-enhanced (LGE) magnetic resonance images (MRI) using only minimal
supervision in a two-step approach. In the first step, we register a small set
of five LGE cardiac magnetic resonance (CMR) images with ground truth labels to
a set of 40 target LGE CMR images without annotation. Each manually annotated
ground truth provides labels of the myocardium and the left ventricle (LV) and
right ventricle (RV) cavities, which are used as atlases. After multi-atlas
label fusion by majority voting, we possess noisy labels for each of the
targeted LGE images. A second set of manual labels exists for 30 patients of
the target LGE CMR images, but are annotated on different MRI sequences (bSSFP
and T2-weighted). Again, we use multi-atlas label fusion with a consistency
constraint to further refine our noisy labels if additional annotations in
other modalities are available for a given patient. In the second step, we
train a deep convolutional network for semantic segmentation on the target data
while using data augmentation techniques to avoid over-fitting to the noisy
labels. After inference and simple post-processing, we achieve our final
segmentation for the targeted LGE CMR images, resulting in an average Dice of
0.890, 0.780, and 0.844 for LV cavity, LV myocardium, and RV cavity,
respectively.
