  Accurate and automated segmentation of intervertebral discs (IVDs) in medical
images is crucial for assessing spine-related disorders, such as osteoporosis,
vertebral fractures, or IVD herniation. We present HCA-Net, a novel contextual
attention network architecture for semantic labeling of IVDs, with a special
focus on exploiting prior geometric information. Our approach excels at
processing features across different scales and effectively consolidating them
to capture the intricate spatial relationships within the spinal cord. To
achieve this, HCA-Net models IVD labeling as a pose estimation problem, aiming
to minimize the discrepancy between each predicted IVD location and its
corresponding actual joint location. In addition, we introduce a skeletal loss
term to reinforce the model's geometric dependence on the spine. This loss
function is designed to constrain the model's predictions to a range that
matches the general structure of the human vertebral skeleton. As a result, the
network learns to reduce the occurrence of false predictions and adaptively
improves the accuracy of IVD location estimation. Through extensive
experimental evaluation on multi-center spine datasets, our approach
consistently outperforms previous state-of-the-art methods on both MRI T1w and
T2w modalities. The codebase is accessible to the public on
\href{https://github.com/xmindflow/HCA-Net}{GitHub}.
