# Generating Synthetic X-ray Images of a Person from the Surface Geometry

arXiv: https://arxiv.org/abs/1805.00553 · HTML: https://mlsanity.com/paper/1805.00553/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2018.

## Built on by (most cited first, 9 of 9)

- [Analyzing Tumors by Synthesis](https://mlsanity.com/paper/2409.06035/) (2024)
- [ReXErr: Synthesizing Clinically Meaningful Errors in Diagnostic Radiology Reports](https://mlsanity.com/paper/2409.10829/) (2024)
- [Synthetic Data in Radiological Imaging: Current State and Future Outlook](https://mlsanity.com/paper/2407.01561/) (2024)
- [CryoGEM: Physics-Informed Generative Cryo-Electron Microscopy](https://mlsanity.com/paper/2312.02235/) (2023)
- [Improving Tuberculosis (TB) Prediction using Synthetically Generated Computed Tomography (CT) Images](https://mlsanity.com/paper/2109.11480/) (2021)
- [Automated CT Lung Cancer Screening Workflow using 3D Camera](https://mlsanity.com/paper/2309.15750/) (2023)
- [MaSkel: A Model for Human Whole-body X-rays Generation from Human Masking Images](https://mlsanity.com/paper/2404.09000/) (2024)
- [Intelligent Scoliosis Screening and Diagnosis: A Survey](https://mlsanity.com/paper/2310.08756/) (2023)
- [LatXGen: Towards Radiation-Free and Accurate Quantitative Analysis of Sagittal Spinal Alignment Via Cross-Modal Radiographic View Synthesis](https://mlsanity.com/paper/2509.24165/) (2025)

## Abstract (arXiv)

We present a novel framework that learns to predict human anatomy from body surface. Specifically, our approach generates a synthetic X-ray image of a person only from the person's surface geometry. Furthermore, the synthetic X-ray image is parametrized and can be manipulated by adjusting a set of body markers which are also generated during the X-ray image prediction. With the proposed framework, multiple synthetic X-ray images can easily be generated by varying surface geometry. By perturbing the parameters, several additional synthetic X-ray images can be generated from the same surface geometry. As a result, our approach offers a potential to overcome the training data barrier in the medical domain. This capability is achieved by learning a pair of networks - one learns to generate the full image from the partial image and a set of parameters, and the other learns to estimate the parameters given the full image. During training, the two networks are trained iteratively such that they would converge to a solution where the predicted parameters and the full image are consistent with each other. In addition to medical data enrichment, our framework can also be used for image completion as well as anomaly detection.