Dense Representation Learning and Differentiable Shape Modeling for Spine Landmark Detection

The paper introduces a method for detecting spine landmarks in X-ray images by treating landmark detection as a pixel-level contrastive representation learning task. Using a generic U‑Net backbone, the approach generates more discriminative heatmaps than traditional heatmap regression, enabling better differentiation between vertebral corners. The method further incorporates learnable shape priors to resolve location ambiguities, achieving competitive accuracy on the AASCE dataset and two private datasets.

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