# X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery

arXiv: https://arxiv.org/abs/1803.08608 · HTML: https://mlsanity.com/paper/1803.08608/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2018.

## Built on by (most cited first, 22 of 22)

- [One-Shot Medical Landmark Detection](https://mlsanity.com/paper/2103.04527/) (2021)
- [The Impact of Machine Learning on 2D/3D Registration for Image-guided Interventions: A Systematic Review and Perspective](https://mlsanity.com/paper/2108.02238/) (2021)
- [Self-Supervised 2D/3D Registration for X-Ray to CT Image Fusion](https://mlsanity.com/paper/2210.07611/) (2022)
- [Pelphix: Surgical Phase Recognition from X-ray Images in Percutaneous Pelvic Fixation](https://mlsanity.com/paper/2304.09285/) (2023)
- [X-Ray to CT Rigid Registration Using Scene Coordinate Regression](https://mlsanity.com/paper/2311.15087/) (2023)
- [Modeling Annotation Uncertainty with Gaussian Heatmaps in Landmark Localization](https://mlsanity.com/paper/2109.09533/) (2021)
- [RayEmb: Arbitrary Landmark Detection in X-Ray Images Using Ray Embedding Subspace](https://mlsanity.com/paper/2410.08152/) (2024)
- [SyntheX: Scaling Up Learning-based X-ray Image Analysis Through In Silico Experiments](https://mlsanity.com/paper/2206.06127/) (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)
- [Automatic Landmarks Correspondence Detection in Medical Images with an Application to Deformable Image Registration](https://mlsanity.com/paper/2109.02722/) (2021)
- [Robust Landmark-based Stent Tracking in X-ray Fluoroscopy](https://mlsanity.com/paper/2207.09933/) (2022)
- [Morphology-Aware Interactive Keypoint Estimation](https://mlsanity.com/paper/2209.07163/) (2022)
- [FM-OSD: Foundation Model-Enabled One-Shot Detection of Anatomical Landmarks](https://mlsanity.com/paper/2407.05412/) (2024)
- [Deep Learning compatible Differentiable X-ray Projections for Inverse Rendering](https://mlsanity.com/paper/2102.02912/) (2021)
- [Anatomical Landmarks Localization for 3D Foot Point Clouds](https://mlsanity.com/paper/2110.00937/) (2021)
- [Intrinsic Tolerance in C-Arm Imaging: How Extrinsic Re-optimization Preserves 3D Reconstruction Accuracy](https://mlsanity.com/paper/2603.14031/) (2026)
- [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)
- [Landmarks Are Alike Yet Distinct: Harnessing Similarity and Individuality for One-Shot Medical Landmark Detection](https://mlsanity.com/paper/2503.16058/) (2025)
- [Robust and Accurate Multi-view 2D/3D Image Registration with Differentiable X-ray Rendering and Dual Cross-view Constraints](https://mlsanity.com/paper/2506.22191/) (2025)
- [2D Versus 3D Diffusion for In Silico Training of Interventional X-ray AI Models](https://mlsanity.com/paper/2606.21414/) (2026)
- [Learning Dense 2D-3D Correspondence for X-ray-to-CT Registration of Knee Bones](https://mlsanity.com/paper/2607.22803/) (2026)

## Abstract (arXiv)

X-ray image guidance enables percutaneous alternatives to complex procedures. Unfortunately, the indirect view onto the anatomy in addition to projective simplification substantially increase the task-load for the surgeon. Additional 3D information such as knowledge of anatomical landmarks can benefit surgical decision making in complicated scenarios. Automatic detection of these landmarks in transmission imaging is challenging since image-domain features characteristic to a certain landmark change substantially depending on the viewing direction. Consequently and to the best of our knowledge, the above problem has not yet been addressed. In this work, we present a method to automatically detect anatomical landmarks in X-ray images independent of the viewing direction. To this end, a sequential prediction framework based on convolutional layers is trained on synthetically generated data of the pelvic anatomy to predict 23 landmarks in single X-ray images. View independence is contingent on training conditions and, here, is achieved on a spherical segment covering (120 x 90) degrees in LAO/RAO and CRAN/CAUD, respectively, centered around AP. On synthetic data, the proposed approach achieves a mean prediction error of 5.6 +- 4.5 mm. We demonstrate that the proposed network is immediately applicable to clinically acquired data of the pelvis. In particular, we show that our intra-operative landmark detection together with pre-operative CT enables X-ray pose estimation which, ultimately, benefits initialization of image-based 2D/3D registration.