  Semi-supervised learning has substantially advanced medical image
segmentation since it alleviates the heavy burden of acquiring the costly
expert-examined annotations. Especially, the consistency-based approaches have
attracted more attention for their superior performance, wherein the real
labels are only utilized to supervise their paired images via supervised loss
while the unlabeled images are exploited by enforcing the perturbation-based
\textit{"unsupervised"} consistency without explicit guidance from those real
labels. However, intuitively, the expert-examined real labels contain more
reliable supervision signals. Observing this, we ask an unexplored but
interesting question: can we exploit the unlabeled data via explicit real label
supervision for semi-supervised training? To this end, we discard the previous
perturbation-based consistency but absorb the essence of non-parametric
prototype learning. Based on the prototypical network, we then propose a novel
cyclic prototype consistency learning (CPCL) framework, which is constructed by
a labeled-to-unlabeled (L2U) prototypical forward process and an
unlabeled-to-labeled (U2L) backward process. Such two processes synergistically
enhance the segmentation network by encouraging more discriminative and compact
features. In this way, our framework turns previous \textit{"unsupervised"}
consistency into new \textit{"supervised"} consistency, obtaining the
\textit{"all-around real label supervision"} property of our method. Extensive
experiments on brain tumor segmentation from MRI and kidney segmentation from
CT images show that our CPCL can effectively exploit the unlabeled data and
outperform other state-of-the-art semi-supervised medical image segmentation
methods.
