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.

翻译：具有良好注释的医学图像成本高昂,有时甚至无法获取,在某种程度上阻碍了里程碑检测的准确性。半监督的学习通过利用未贴标签的数据来了解解剖界标的人口结构,减轻了对大规模附加说明数据的依赖。全球形状制约是解剖界标的固有特性,这些特性为更一致的未贴标签数据的伪标签提供了宝贵的指导,而以前半监督方法对此置若罔闻。在本文中,我们提出了一个模型-不可知形状调节自训练框架,通过充分考虑全球形状限制,进行半监督的标志性检测。具体地说,为了确保假标签可靠和一致,以五氯苯甲醚为基础的形状模型调整假标签并消除异常标签。新的区域注意,使网络自动侧重于在假标签周围的结构一致的区域。广泛的实验表明,我们的方法优于其他半监督方法,并在三个医疗图像数据集上取得了显著的改进。此外,我们的框架是灵活的,可以用作一个插件模块,作为最受监督的方法,以便进一步改进性能。