Structure-Aware Long Short-Term Memory Network for 3D Cephalometric Landmark Detection

by   Runnan Chen, et al.

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 high-resolution cropped patches. To boost accuracy, SA-LSTM captures global-local dependence among the cropping patches via self-attention. Specifically, a graph attention module implicitly encodes the landmark's global structure to rationalize the predicted position. Furthermore, a novel attention-gated module recursively filters irrelevant local features and maintains high-confident local predictions for aggregating the final result. Experiments show that our method significantly outperforms state-of-the-art methods in terms of efficiency and accuracy on an in-house dataset and a public dataset, achieving 1.64 mm and 2.37 mm average errors, respectively, and using only 0.5 seconds for inferring the whole CBCT volume of resolution 768*768*576. Moreover, all predicted landmarks are within 8 mm error, which is vital for acceptable cephalometric analysis.


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