  Detecting 3D landmarks on cone-beam computed tomography (CBCT) is crucial to
assessing and quantifying the anatomical abnormalities in 3D cephalometric
analysis. However, the current methods are time-consuming and suffer from large
biases in landmark localization, leading to unreliable diagnosis results. In
this work, we propose a novel Structure-Aware Long Short-Term Memory framework
(SA-LSTM) for efficient and accurate 3D landmark detection. To reduce the
computational burden, SA-LSTM is designed in two stages. It first locates the
coarse landmarks via heatmap regression on a down-sampled CBCT volume and then
progressively refines landmarks by attentive offset regression using
multi-resolution cropped patches. To boost accuracy, SA-LSTM captures
global-local dependence among the cropping patches via self-attention.
Specifically, a novel graph attention module implicitly encodes the landmark's
global structure to rationalize the predicted position. Moreover, a novel
attention-gated module recursively filters irrelevant local features and
maintains high-confident local predictions for aggregating the final result.
Experiments conducted on an in-house dataset and a public dataset show that our
method outperforms state-of-the-art methods, achieving 1.64 mm and 2.37 mm
average errors, respectively. Furthermore, our method is very efficient, taking
only 0.5 seconds for inferring the whole CBCT volume of resolution
768$\times$768$\times$576.
