Abstract:
Cephalometric analysis is a cornerstone of modern orthodontics, enabling precise diagnosis and
treatment planning by identifying key morphometric landmarks. Since manual identification of
these landmarks is labor-intensive and prone to variability, developing automated systems has
been essential. Existing automated solutions often fall short of clinical requirements due to challenges
such as small, insufficiently diverse datasets, poor generalizability, and computational
inefficiency. Addressing these gaps, we propose three distinct methodologies for automated
cephalometric landmark detection, each addressing specific limitations and building upon the
strengths of the previous one. The frameworks leverage the Aariz dataset, known for its diverse
resolutions and spatial variations. The final, most optimized methodology proposes a three-stage
cascaded framework that seamlessly integrates global and local feature learning while ensuring
invariance to resolution and spatial dimensions. In the first stage, the craniofacial region is
extracted by predicting the locations of edge landmarks, achieving spatial invariance. This identified
region is then fed to the second stage, where a multi-scale CNN performs simultaneous
landmark prediction through heatmap regression, introducing resolution invariance and enhancing
global feature learning. These pseudo-predictions are then used to generate high-resolution
crops from the original image around the predicted landmark locations. The final stage refines
the coarse landmark predictions for all landmarks using a single model optimized for local
feature learning, further enhancing computational efficiency. The framework demonstrates robust
generalizability across diverse cephalograms, achieving clinically acceptable performance
on multiple datasets. On the Aariz dataset, our method achieved an overall mean radial error
(MRE) of 1.69 ± 3.36 mm with 81.18% landmarks falling within the clinically acceptable range
of 2 mm. These results highlight the framework’s reliability for landmark detection and its success
in handling diverse cephalograms, positioning it as a practical solution for clinical use that
streamlines workflows and enables timely, accurate treatment planning.
