  Deep learning based semi-supervised learning (SSL) methods have achieved
strong performance in medical image segmentation, which can alleviate doctors'
expensive annotation by utilizing a large amount of unlabeled data. Unlike most
existing semi-supervised learning methods, adversarial training based methods
distinguish samples from different sources by learning the data distribution of
the segmentation map, leading the segmenter to generate more accurate
predictions. We argue that the current performance restrictions for such
approaches are the problems of feature extraction and learning preference. In
this paper, we propose a new semi-supervised adversarial method called Patch
Confidence Adversarial Training (PCA) for medical image segmentation. Rather
than single scalar classification results or pixel-level confidence maps, our
proposed discriminator creates patch confidence maps and classifies them at the
scale of the patches. The prediction of unlabeled data learns the pixel
structure and context information in each patch to get enough gradient
feedback, which aids the discriminator in convergent to an optimal state and
improves semi-supervised segmentation performance. Furthermore, at the
discriminator's input, we supplement semantic information constraints on
images, making it simpler for unlabeled data to fit the expected data
distribution. Extensive experiments on the Automated Cardiac Diagnosis
Challenge (ACDC) 2017 dataset and the Brain Tumor Segmentation (BraTS) 2019
challenge dataset show that our method outperforms the state-of-the-art
semi-supervised methods, which demonstrates its effectiveness for medical image
segmentation.
