Revisiting Cephalometric Landmark Detection from the view of Human Pose
  Estimation with Lightweight Super-Resolution Head
Qian Wu Si Yong Yeo Yufei Chen Jun Liu
Accurate localization of cephalometric landmarks holds great importance in
the fields of orthodontics and orthognathics due to its potential for
automating key point labeling. In the context of landmark detection,
particularly in cephalometrics, it has been observed that existing methods
often lack standardized pipelines and well-designed bias reduction processes,
which significantly impact their performance. In this paper, we revisit a
related task, human pose estimation (HPE), which shares numerous similarities
with cephalometric landmark detection (CLD), and emphasize the potential for
transferring techniques from the former field to benefit the latter. Motivated
by this insight, we have developed a robust and adaptable benchmark based on
the well-established HPE codebase known as MMPose. This benchmark can serve as
a dependable baseline for achieving exceptional CLD performance. Furthermore,
we introduce an upscaling design within the framework to further enhance
performance. This enhancement involves the incorporation of a lightweight and
efficient super-resolution module, which generates heatmap predictions on
high-resolution features and leads to further performance refinement,
benefiting from its ability to reduce quantization bias. In the MICCAI
CLDetection2023 challenge, our method achieves 1st place ranking on three
metrics and 3rd place on the remaining one. The code for our method is
available at https://github.com/5k5000/CLdetection2023.