# An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms

arXiv: https://arxiv.org/abs/1906.07549 · HTML: https://mlsanity.com/paper/1906.07549/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2019.

## Built on by (most cited first, 27 of 27)

- [One-Shot Medical Landmark Detection](https://mlsanity.com/paper/2103.04527/) (2021)
- [Cephalometric Landmark Detection across Ages with Prototypical Network](https://mlsanity.com/paper/2406.12577/) (2024)
- [Modeling Annotation Uncertainty with Gaussian Heatmaps in Landmark Localization](https://mlsanity.com/paper/2109.09533/) (2021)
- [Uncertainty Estimation for Heatmap-based Landmark Localization](https://mlsanity.com/paper/2203.02351/) (2022)
- [CEPHA29: Automatic Cephalometric Landmark Detection Challenge 2023](https://mlsanity.com/paper/2212.04808/) (2022)
- [Structure-Guided Self-Supervised Matching for One-Shot Medical Landmark Detection](https://mlsanity.com/paper/2203.01687/) (2022)
- [PELE scores: Pelvic X-ray Landmark Detection by Pelvis Extraction and Enhancement](https://mlsanity.com/paper/2305.04294/) (2023)
- [SAM++: Enhancing Anatomic Matching using Semantic Information and Structural Inference](https://mlsanity.com/paper/2306.13988/) (2023)
- [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)
- [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)
- [landmarker: a Toolkit for Anatomical Landmark Localization in 2D/3D Images](https://mlsanity.com/paper/2501.10098/) (2025)
- [Reliable uncertainty quantification for 2D/3D anatomical landmark localization using multi-output conformal prediction](https://mlsanity.com/paper/2503.14106/) (2025)
- [Synergistic Bleeding Region and Point Detection in Laparoscopic Surgical Videos](https://mlsanity.com/paper/2503.22174/) (2025)
- [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)
- [Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Anthropic Prior Knowledge](https://mlsanity.com/paper/2404.01013/) (2024)
- [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)
- [SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal Segmentation](https://mlsanity.com/paper/2306.08213/) (2023)
- [Salt & Pepper Heatmaps: Diffusion-informed Landmark Detection Strategy](https://mlsanity.com/paper/2407.09192/) (2024)
- [Tracing Like a Clinician: Anatomy-Guided Spatial Priors for Cephalometric Landmark Detection](https://mlsanity.com/paper/2605.03358/) (2026)

## Abstract (arXiv)

Cephalometric tracing method is usually used in orthodontic diagnosis and treatment planning. In this paper, we propose a deep learning based framework to automatically detect anatomical landmarks in cephalometric X-ray images. We train the deep encoder-decoder for landmark detection, and combine global landmark configuration with local high-resolution feature responses. The proposed frame-work is based on 2-stage u-net, regressing the multi-channel heatmaps for land-mark detection. In this framework, we embed attention mechanism with global stage heatmaps, guiding the local stage inferring, to regress the local heatmap patches in a high resolution. Besides, the Expansive Exploration strategy improves robustness while inferring, expanding the searching scope without increasing model complexity. We have evaluated our framework in the most widely-used public dataset of landmark detection in cephalometric X-ray images. With less computation and manually tuning, our framework achieves state-of-the-art results.