  Semi-supervised learning has attracted great attention in the field of
machine learning, especially for medical image segmentation tasks, since it
alleviates the heavy burden of collecting abundant densely annotated data for
training. However, most of existing methods underestimate the importance of
challenging regions (e.g. small branches or blurred edges) during training. We
believe that these unlabeled regions may contain more crucial information to
minimize the uncertainty prediction for the model and should be emphasized in
the training process. Therefore, in this paper, we propose a novel Mutual
Consistency Network (MC-Net) for semi-supervised left atrium segmentation from
3D MR images. Particularly, our MC-Net consists of one encoder and two slightly
different decoders, and the prediction discrepancies of two decoders are
transformed as an unsupervised loss by our designed cycled pseudo label scheme
to encourage mutual consistency. Such mutual consistency encourages the two
decoders to have consistent and low-entropy predictions and enables the model
to gradually capture generalized features from these unlabeled challenging
regions. We evaluate our MC-Net on the public Left Atrium (LA) database and it
obtains impressive performance gains by exploiting the unlabeled data
effectively. Our MC-Net outperforms six recent semi-supervised methods for left
atrium segmentation, and sets the new state-of-the-art performance on the LA
database.
