  Purpose: We perform anatomical landmarking for craniomaxillofacial (CMF)
bones without explicitly segmenting them. Towards this, we propose a new simple
yet efficient deep network architecture, called \textit{relational reasoning
network (RRN)}, to accurately learn the local and the global relations among
the landmarks in CMF bones; specifically, mandible, maxilla, and nasal bones.
  Approach: The proposed RRN works in an end-to-end manner, utilizing learned
relations of the landmarks based on dense-block units. For a given few
landmarks as input, RRN treats the landmarking process similar to a data
imputation problem where predicted landmarks are considered missing.
  Results: We applied RRN to cone beam computed tomography scans obtained from
250 patients. With a 4-fold cross validation technique, we obtained an average
root mean squared error of less than 2 mm per landmark. Our proposed RRN has
revealed unique relationships among the landmarks that help us in inferring
several \textit{reasoning} about informativeness of the landmark points. The
proposed system identifies the missing landmark locations accurately even when
severe pathology or deformation are present in the bones.
  Conclusions: Accurately identifying anatomical landmarks is a crucial step in
deformation analysis and surgical planning for CMF surgeries. Achieving this
goal without the need for explicit bone segmentation addresses a major
limitation of segmentation based approaches, where segmentation failure (as
often the case in bones with severe pathology or deformation) could easily lead
to incorrect landmarking. To the best of our knowledge, this is the first of
its kind algorithm finding anatomical relations of the objects using deep
learning.
