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.

翻译：头影测量标志的准确定位在正畸和正颌外科领域具有重要意义，因为它具有自动标注关键点的潜力。在标志检测（尤其是头影测量学）的背景下，现有方法往往缺乏标准化的流程和精心设计的偏差减少过程，这严重影响了其性能。在本文中，我们重新审视了一项相关任务——人体姿态估计（HPE），该任务与头影测量标志检测（CLD）有许多相似之处，并强调了将前者领域的技术迁移以惠及后者的潜力。受此启发，我们基于成熟的HPE代码库MMPose开发了一个稳健且适应性强的基准测试。该基准可作为实现卓越CLD性能的可靠基线。此外，我们在框架中引入了一个放大设计以进一步提升性能。这一增强包括加入一个轻量且高效的超分辨率模块，该模块在高分辨率特征上生成热力图预测，从而通过减少量化偏差来进一步优化性能。在MICCAI CLDetection2023挑战赛中，我们的方法在三个指标上获得第一名，在剩余一个指标上获得第三名。我们的方法代码可在https://github.com/5k5000/CLdetection2023获取。