  Vertebral body compression fractures are early signs of osteoporosis. Though
these fractures are visible on Computed Tomography (CT) images, they are
frequently missed by radiologists in clinical settings. Prior research on
automatic methods of vertebral fracture classification proves its reliable
quality; however, existing methods provide hard-to-interpret outputs and
sometimes fail to process cases with severe abnormalities such as highly
pathological vertebrae or scoliosis. We propose a new two-step algorithm to
localize the vertebral column in 3D CT images and then detect individual
vertebrae and quantify fractures in 2D simultaneously. We train neural networks
for both steps using a simple 6-keypoints based annotation scheme, which
corresponds precisely to the current clinical recommendation. Our algorithm has
no exclusion criteria, processes 3D CT in 2 seconds on a single GPU, and
provides an interpretable and verifiable output. The method approaches
expert-level performance and demonstrates state-of-the-art results in vertebrae
3D localization (the average error is 1 mm), vertebrae 2D detection (precision
and recall are 0.99), and fracture identification (ROC AUC at the patient level
is up to 0.96). Our anchor-free vertebra detection network shows excellent
generalizability on a new domain by achieving ROC AUC 0.95, sensitivity 0.85,
specificity 0.9 on a challenging VerSe dataset with many unseen vertebra types.
