Revisiting Cephalometric Landmark Detection from the view of Human Pose Estimation with Lightweight Super-Resolution Head

• URL: http://arxiv.org/abs/2309.17143v1
• Date: Fri, 29 Sep 2023 11:15:39 GMT
• Title: Revisiting Cephalometric Landmark Detection from the view of Human Pose Estimation with Lightweight Super-Resolution Head
• Authors: Qian Wu and Si Yong Yeo and Yufei Chen and Jun Liu
• Abstract summary: We develop a benchmark based on the well-established human pose estimation (HPE) known as MMPose. We introduce an upscaling design within the framework to further enhance performance. In the MICCAI CLDetection2023 challenge, our method achieves 1st place ranking on three metrics and 3rd place on the remaining one.
• Score: 11.40242574405714
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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