  In clinical radiology reports, doctors capture important information about
the patient's health status. They convey their observations from raw medical
imaging data about the inner structures of a patient. As such, formulating
reports requires medical experts to possess wide-ranging knowledge about
anatomical regions with their normal, healthy appearance as well as the ability
to recognize abnormalities. This explicit grasp on both the patient's anatomy
and their appearance is missing in current medical image-processing systems as
annotations are especially difficult to gather. This renders the models to be
narrow experts e.g. for identifying specific diseases. In this work, we recover
this missing link by adding human anatomy into the mix and enable the
association of content in medical reports to their occurrence in associated
imagery (medical phrase grounding). To exploit anatomical structures in this
scenario, we present a sophisticated automatic pipeline to gather and integrate
human bodily structures from computed tomography datasets, which we incorporate
in our PAXRay: A Projected dataset for the segmentation of Anatomical
structures in X-Ray data. Our evaluation shows that methods that take advantage
of anatomical information benefit heavily in visually grounding radiologists'
findings, as our anatomical segmentations allow for up to absolute 50% better
grounding results on the OpenI dataset as compared to commonly used region
proposals. The PAXRay dataset is available at
https://constantinseibold.github.io/paxray/.
