# Cephalometric Landmark Detection by AttentiveFeature Pyramid Fusion and Regression-Voting

arXiv: https://arxiv.org/abs/1908.08841 · HTML: https://mlsanity.com/paper/1908.08841/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2019.

## Built on by (most cited first, 30 of 32)

- [One-Shot Medical Landmark Detection](https://mlsanity.com/paper/2103.04527/) (2021)
- [Semi-supervised Anatomical Landmark Detection via Shape-regulated Self-training](https://mlsanity.com/paper/2105.13593/) (2021)
- [One-Shot Medical Landmark Localization by Edge-Guided Transform and Noisy Landmark Refinement](https://mlsanity.com/paper/2208.00453/) (2022)
- [Learn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images](https://mlsanity.com/paper/2105.09124/) (2021)
- [Modeling Annotation Uncertainty with Gaussian Heatmaps in Landmark Localization](https://mlsanity.com/paper/2109.09533/) (2021)
- [CEPHA29: Automatic Cephalometric Landmark Detection Challenge 2023](https://mlsanity.com/paper/2212.04808/) (2022)
- [Revisiting Cephalometric Landmark Detection from the view of Human Pose Estimation with Lightweight Super-Resolution Head](https://mlsanity.com/paper/2309.17143/) (2023)
- [Structure-Guided Self-Supervised Matching for One-Shot Medical Landmark Detection](https://mlsanity.com/paper/2203.01687/) (2022)
- [Unsupervised Domain Adaptation for Anatomical Landmark Detection](https://mlsanity.com/paper/2308.13286/) (2023)
- [Feature Aggregation and Refinement Network for 2D AnatomicalLandmark Detection](https://mlsanity.com/paper/2111.00659/) (2021)
- [Dense Representative Tooth Landmark/axis Detection Network on 3D Model](https://mlsanity.com/paper/2111.04212/) (2021)
- [Morphology-Aware Interactive Keypoint Estimation](https://mlsanity.com/paper/2209.07163/) (2022)
- ['Aariz: A Benchmark Dataset for Automatic Cephalometric Landmark Detection and CVM Stage Classification](https://mlsanity.com/paper/2302.07797/) (2023)
- [Rethinking Self-training for Semi-supervised Landmark Detection: A Selection-free Approach](https://mlsanity.com/paper/2404.04556/) (2024)
- [Structure-Aware Long Short-Term Memory Network for 3D Cephalometric Landmark Detection](https://mlsanity.com/paper/2107.09899/) (2021)
- [Facial Anatomical Landmark Detection using Regularized Transfer Learning with Application to Fetal Alcohol Syndrome Recognition](https://mlsanity.com/paper/2109.05485/) (2021)
- [Uncertainty-aware U-Net for Medical Landmark Detection](https://mlsanity.com/paper/2303.10349/) (2023)
- [EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiograms](https://mlsanity.com/paper/2307.12229/) (2023)
- [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)
- [Optimising for the Unknown: Domain Alignment for Cephalometric Landmark Detection](https://mlsanity.com/paper/2410.04445/) (2024)
- [Reliability of deep learning models for anatomical landmark detection: The role of inter-rater variability](https://mlsanity.com/paper/2411.17850/) (2024)
- [Convolutional Neural Networks in Orthodontics: a review](https://mlsanity.com/paper/2104.08886/) (2021)
- [Leveraging Clinically Relevant Biometric Constraints To Supervise A Deep Learning Model For The Accurate Caliper Placement To Obtain Sonographic Measurements Of The Fetal Brain](https://mlsanity.com/paper/2203.14482/) (2022)
- [Multi-Resolution Fusion for Fully Automatic Cephalometric Landmark Detection](https://mlsanity.com/paper/2310.02855/) (2023)
- [Salt & Pepper Heatmaps: Diffusion-informed Landmark Detection Strategy](https://mlsanity.com/paper/2407.09192/) (2024)
- [Self-CephaloNet: A Two-stage Novel Framework using Operational Neural Network for Cephalometric Analysis](https://mlsanity.com/paper/2501.10984/) (2025)
- [Two-Steps Neural Networks for an Automated Cerebrovascular Landmark Detection](https://mlsanity.com/paper/2507.02349/) (2025)
- [CephRes-MHNet: A Multi-Head Residual Network for Accurate and Robust Cephalometric Landmark Detection](https://mlsanity.com/paper/2511.10173/) (2025)
- [Tracing Like a Clinician: Anatomy-Guided Spatial Priors for Cephalometric Landmark Detection](https://mlsanity.com/paper/2605.03358/) (2026)

## Abstract (arXiv)

Marking anatomical landmarks in cephalometric radiography is a critical operation in cephalometric analysis. Automatically and accurately locating these landmarks is a challenging issue because different landmarks require different levels of resolution and semantics. Based on this observation, we propose a novel attentive feature pyramid fusion module (AFPF) to explicitly shape high-resolution and semantically enhanced fusion features to achieve significantly higher accuracy than existing deep learning-based methods. We also combine heat maps and offset maps to perform pixel-wise regression-voting to improve detection accuracy. By incorporating the AFPF and regression-voting, we develop an end-to-end deep learning framework that improves detection accuracy by 7%~11% for all the evaluation metrics over the state-of-the-art method. We present ablation studies to give more insights into different components of our method and demonstrate its generalization capability and stability for unseen data from diverse devices.