  Well-annotated medical images are costly and sometimes even impossible to
acquire, hindering landmark detection accuracy to some extent. Semi-supervised
learning alleviates the reliance on large-scale annotated data by exploiting
the unlabeled data to understand the population structure of anatomical
landmarks. The global shape constraint is the inherent property of anatomical
landmarks that provides valuable guidance for more consistent pseudo labelling
of the unlabeled data, which is ignored in the previously semi-supervised
methods. In this paper, we propose a model-agnostic shape-regulated
self-training framework for semi-supervised landmark detection by fully
considering the global shape constraint. Specifically, to ensure pseudo labels
are reliable and consistent, a PCA-based shape model adjusts pseudo labels and
eliminate abnormal ones. A novel Region Attention loss to make the network
automatically focus on the structure consistent regions around pseudo labels.
Extensive experiments show that our approach outperforms other semi-supervised
methods and achieves notable improvement on three medical image datasets.
Moreover, our framework is flexible and can be used as a plug-and-play module
integrated into most supervised methods to improve performance further.
