  Scoliosis is a congenital disease that causes lateral curvature in the spine.
Its assessment relies on the identification and localization of vertebrae in
spinal X-ray images, conventionally via tedious and time-consuming manual
radiographic procedures that are prone to subjectivity and observational
variability. Reliability can be improved through the automatic detection and
localization of spinal landmarks. To guide a CNN in the learning of spinal
shape while detecting landmarks in X-ray images, we propose a novel loss based
on a bipartite distance (BPD) measure, and show that it consistently improves
landmark detection performance.
