  Accurately segmenting teeth and identifying the corresponding anatomical
landmarks on dental mesh models are essential in computer-aided orthodontic
treatment. Manually performing these two tasks is time-consuming, tedious, and,
more importantly, highly dependent on orthodontists' experiences due to the
abnormality and large-scale variance of patients' teeth. Some machine
learning-based methods have been designed and applied in the orthodontic field
to automatically segment dental meshes (e.g., intraoral scans). In contrast,
the number of studies on tooth landmark localization is still limited. This
paper proposes a two-stage framework based on mesh deep learning (called
TS-MDL) for joint tooth labeling and landmark identification on raw intraoral
scans. Our TS-MDL first adopts an end-to-end \emph{i}MeshSegNet method (i.e., a
variant of the existing MeshSegNet with both improved accuracy and efficiency)
to label each tooth on the downsampled scan. Guided by the segmentation
outputs, our TS-MDL further selects each tooth's region of interest (ROI) on
the original mesh to construct a light-weight variant of the pioneering
PointNet (i.e., PointNet-Reg) for regressing the corresponding landmark
heatmaps. Our TS-MDL was evaluated on a real-clinical dataset, showing
promising segmentation and localization performance. Specifically,
\emph{i}MeshSegNet in the first stage of TS-MDL reached an averaged Dice
similarity coefficient (DSC) at \textcolor[rgb]{0,0,0}{$0.964\pm0.054$},
significantly outperforming the original MeshSegNet. In the second stage,
PointNet-Reg achieved a mean absolute error (MAE) of $0.597\pm0.761 \, mm$ in
distances between the prediction and ground truth for $66$ landmarks, which is
superior compared with other networks for landmark detection. All these results
suggest the potential usage of our TS-MDL in orthodontics.
