Few-shot medical image segmentation has achieved great progress in improving
accuracy and efficiency of medical analysis in the biomedical imaging field.
However, most existing methods cannot explore inter-class relations among base
and novel medical classes to reason unseen novel classes. Moreover, the same
kind of medical class has large intra-class variations brought by diverse
appearances, shapes and scales, thus causing ambiguous visual characterization
to degrade generalization performance of these existing methods on unseen novel
classes. To address the above challenges, in this paper, we propose a
\underline{\textbf{P}}rototype correlation \underline{\textbf{M}}atching and
\underline{\textbf{C}}lass-relation \underline{\textbf{R}}easoning (i.e.,
\textbf{PMCR}) model. The proposed model can effectively mitigate false pixel
correlation matches caused by large intra-class variations while reasoning
inter-class relations among different medical classes. Specifically, in order
to address false pixel correlation match brought by large intra-class
variations, we propose a prototype correlation matching module to mine
representative prototypes that can characterize diverse visual information of
different appearances well. We aim to explore prototype-level rather than
pixel-level correlation matching between support and query features via optimal
transport algorithm to tackle false matches caused by intra-class variations.
Meanwhile, in order to explore inter-class relations, we design a
class-relation reasoning module to segment unseen novel medical objects via
reasoning inter-class relations between base and novel classes. Such
inter-class relations can be well propagated to semantic encoding of local
query features to improve few-shot segmentation performance. Quantitative
comparisons illustrates the large performance improvement of our model over
other baseline methods.

通过原型相关性匹配和类别关系推理模型，解决少样本医学图像分割中类内变异和类间关系推理的挑战，提高分割性能。