  In this paper, we propose a novel mutual consistency network (MC-Net+) to
effectively exploit the unlabeled data for semi-supervised medical image
segmentation. The MC-Net+ model is motivated by the observation that deep
models trained with limited annotations are prone to output highly uncertain
and easily mis-classified predictions in the ambiguous regions (e.g., adhesive
edges or thin branches) for medical image segmentation. Leveraging these
challenging samples can make the semi-supervised segmentation model training
more effective. Therefore, our proposed MC-Net+ model consists of two new
designs. First, the model contains one shared encoder and multiple slightly
different decoders (i.e., using different up-sampling strategies). The
statistical discrepancy of multiple decoders' outputs is computed to denote the
model's uncertainty, which indicates the unlabeled hard regions. Second, we
apply a novel mutual consistency constraint between one decoder's probability
output and other decoders' soft pseudo labels. In this way, we minimize the
discrepancy of multiple outputs (i.e., the model uncertainty) during training
and force the model to generate invariant results in such challenging regions,
aiming at regularizing the model training. We compared the segmentation results
of our MC-Net+ model with five state-of-the-art semi-supervised approaches on
three public medical datasets. Extension experiments with two standard
semi-supervised settings demonstrate the superior performance of our model over
other methods, which sets a new state of the art for semi-supervised medical
image segmentation. Our code is released publicly at
https://github.com/ycwu1997/MC-Net.
