  Heatmap-based anatomical landmark detection is still facing two unresolved
challenges: 1) inability to accurately evaluate the distribution of heatmap; 2)
inability to effectively exploit global spatial structure information. To
address the computational inability challenge, we propose a novel
position-aware and sample-aware central loss. Specifically, our central loss
can absorb position information, enabling accurate evaluation of the heatmap
distribution. More advanced is that our central loss is sample-aware, which can
adaptively distinguish easy and hard samples and make the model more focused on
hard samples while solving the challenge of extreme imbalance between landmarks
and non-landmarks. To address the challenge of ignoring structure information,
a Coordinated Transformer, called CoorTransformer, is proposed, which
establishes long-range dependencies under the guidance of landmark coordination
information, making the attention more focused on the sparse landmarks while
taking advantage of global spatial structure. Furthermore, CoorTransformer can
speed up convergence, effectively avoiding the defect that Transformers have
difficulty converging in sparse representation learning. Using the advanced
CoorTransformer and central loss, we propose a generalized detection model that
can handle various scenarios, inherently exploiting the underlying relationship
between landmarks and incorporating rich structural knowledge around the target
landmarks. We analyzed and evaluated CoorTransformer and central loss on three
challenging landmark detection tasks. The experimental results show that our
CoorTransformer outperforms state-of-the-art methods, and the central loss
significantly improves the performance of the model with p-values< 0.05.
