  Detecting anatomical landmarks in medical images plays an essential role in
understanding the anatomy and planning automated processing. In recent years, a
variety of deep neural network methods have been developed to detect landmarks
automatically. However, all of those methods are unary in the sense that a
highly specialized network is trained for a single task say associated with a
particular anatomical region. In this work, for the first time, we investigate
the idea of "You Only Learn Once (YOLO)" and develop a universal anatomical
landmark detection model to realize multiple landmark detection tasks with
end-to-end training based on mixed datasets. The model consists of a local
network and a global network: The local network is built upon the idea of
universal UNet to learn multi-domain local features and the global network is a
parallelly-duplicated sequential of dilated convolutions that extract global
features to further disambiguate the landmark locations. It is worth mentioning
that the new model design requires fewer parameters than models with standard
convolutions to train. We evaluate our YOLO model on three X-ray datasets of
1,588 images on the head, hand, and chest, collectively contributing 62
landmarks. The experimental results show that our proposed universal model
behaves largely better than any previous models trained on multiple datasets.
It even beats the performance of the model that is trained separately for every
single dataset.
