  Semi-supervised learning for medical image segmentation is an important area
of research for alleviating the huge cost associated with the construction of
reliable large-scale annotations in the medical domain. Recent semi-supervised
approaches have demonstrated promising results by employing consistency
regularization, pseudo-labeling techniques, and adversarial learning. These
methods primarily attempt to learn the distribution of labeled and unlabeled
data by enforcing consistency in the predictions or embedding context. However,
previous approaches have focused only on local discrepancy minimization or
context relations across single classes. In this paper, we introduce a novel
adversarial learning-based semi-supervised segmentation method that effectively
embeds both local and global features from multiple hidden layers and learns
context relations between multiple classes. Our voxel-wise adversarial learning
method utilizes a voxel-wise feature discriminator, which considers multilayer
voxel-wise features (involving both local and global features) as an input by
embedding class-specific voxel-wise feature distribution. Furthermore, we
improve our previous representation learning method by overcoming information
loss and learning stability problems, which enables rich representations of
labeled data. Our method outperforms current best-performing state-of-the-art
semi-supervised learning approaches on the image segmentation of the left
atrium (single class) and multiorgan datasets (multiclass). Moreover, our
visual interpretation of the feature space demonstrates that our proposed
method enables a well-distributed and separated feature space from both labeled
and unlabeled data, which improves the overall prediction results.
