  Accurate anatomical landmark detection plays an increasingly vital role in
medical image analysis. Although existing methods achieve satisfying
performance, they are mostly based on CNN and specialized for a single domain
say associated with a particular anatomical region. In this work, we propose a
universal model for multi-domain landmark detection by taking advantage of
transformer for modeling long dependencies and develop a domain-adaptive
transformer model, named as DATR, which is trained on multiple mixed datasets
from different anatomies and capable of detecting landmarks of any image from
those anatomies. The proposed DATR exhibits three primary features: (i) It is
the first universal model which introduces transformer as an encoder for
multi-anatomy landmark detection; (ii) We design a domain-adaptive transformer
for anatomy-aware landmark detection, which can be effectively extended to any
other transformer network; (iii) Following previous studies, we employ a
light-weighted guidance network, which encourages the transformer network to
detect more accurate landmarks. We carry out experiments on three widely used
X-ray datasets for landmark detection, which have 1,588 images and 62 landmarks
in total, including three different anatomies (head, hand, and chest).
Experimental results demonstrate that our proposed DATR achieves
state-of-the-art performances by most metrics and behaves much better than any
previous convolution-based models. The code will be released publicly.
