  Accurate vertebra localization and identification are required in many
clinical applications of spine disorder diagnosis and surgery planning.
However, significant challenges are posed in this task by highly varying
pathologies (such as vertebral compression fracture, scoliosis, and vertebral
fixation) and imaging conditions (such as limited field of view and metal
streak artifacts). This paper proposes a robust and accurate method that
effectively exploits the anatomical knowledge of the spine to facilitate
vertebra localization and identification. A key point localization model is
trained to produce activation maps of vertebra centers. They are then
re-sampled along the spine centerline to produce spine-rectified activation
maps, which are further aggregated into 1-D activation signals. Following this,
an anatomically-constrained optimization module is introduced to jointly search
for the optimal vertebra centers under a soft constraint that regulates the
distance between vertebrae and a hard constraint on the consecutive vertebra
indices. When being evaluated on a major public benchmark of 302 highly
pathological CT images, the proposed method reports the state of the art
identification (id.) rate of 97.4%, and outperforms the best competing method
of 94.7% id. rate by reducing the relative id. error rate by half.
