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

arXiv: https://arxiv.org/abs/2309.17143 · HTML: https://mlsanity.com/paper/2309.17143/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 36 references that are in the map.
- First posted on arXiv 29 Sep 2023.

## Builds on (most cited first, 23 of 23)

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Training Region-based Object Detectors with Online Hard Example Mining](https://mlsanity.com/paper/1604.03540/) (2016)
- [NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding](https://mlsanity.com/paper/1905.04757/) (2019)
- [Distribution-Aware Coordinate Representation for Human Pose Estimation](https://mlsanity.com/paper/1910.06278/) (2019)
- [Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition](https://mlsanity.com/paper/1607.07043/) (2016)
- [Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression](https://mlsanity.com/paper/1904.07399/) (2019)
- [DiffPose: Toward More Reliable 3D Pose Estimation](https://mlsanity.com/paper/2211.16940/) (2022)
- [STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training](https://mlsanity.com/paper/2304.06716/) (2023)
- [The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation](https://mlsanity.com/paper/1911.07524/) (2019)
- [Integrating Spatial Configuration into Heatmap Regression Based CNNs for Landmark Localization](https://mlsanity.com/paper/1908.00748/) (2019)
- [Cephalometric Landmark Detection by AttentiveFeature Pyramid Fusion and Regression-Voting](https://mlsanity.com/paper/1908.08841/) (2019)
- [You Only Learn Once: Universal Anatomical Landmark Detection](https://mlsanity.com/paper/2103.04657/) (2021)
- [Feature Boosting Network For 3D Pose Estimation](https://mlsanity.com/paper/1901.04877/) (2019)
- [Extending nnU-Net is all you need](https://mlsanity.com/paper/2208.10791/) (2022)
- [Which images to label for few-shot medical landmark detection?](https://mlsanity.com/paper/2112.04386/) (2021)
- [Heatmap Distribution Matching for Human Pose Estimation](https://mlsanity.com/paper/2210.00740/) (2022)
- [UOD: Universal One-shot Detection of Anatomical Landmarks](https://mlsanity.com/paper/2306.07615/) (2023)
- [Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT Scans](https://mlsanity.com/paper/2210.07490/) (2022)
- [Lightweight Super-Resolution Head for Human Pose Estimation](https://mlsanity.com/paper/2307.16765/) (2023)
- [Unsupervised Domain Adaptation for Anatomical Landmark Detection](https://mlsanity.com/paper/2308.13286/) (2023)

## Built on by (most cited first, 6 of 6)

- [Cephalometric Landmark Detection across Ages with Prototypical Network](https://mlsanity.com/paper/2406.12577/) (2024)
- [Towards Better Cephalometric Landmark Detection with Diffusion Data Generation](https://mlsanity.com/paper/2505.06055/) (2025)
- [Deep Learning Techniques for Automatic Lateral X-ray Cephalometric Landmark Detection: Is the Problem Solved?](https://mlsanity.com/paper/2409.15834/) (2024)
- [HYATT-Net is Grand: A Hybrid Attention Network for Performant Anatomical Landmark Detection](https://mlsanity.com/paper/2412.06499/) (2024)
- [CephRes-MHNet: A Multi-Head Residual Network for Accurate and Robust Cephalometric Landmark Detection](https://mlsanity.com/paper/2511.10173/) (2025)
- [LM-PCVMNet: Pediatric Cervical Vertebral Maturation Analysis with Deep Fusion of Landmarks and Metadata](https://mlsanity.com/paper/2609.16033/) (2026)

## Abstract (arXiv)

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.