Synthetic Data Generated from CT Scans for Patient Pose Assessment
Abstract
An adequate diagnostic quality of radiographs is essential for reliable diagnoses and treatment planning. The patient's pose during radiography is one of the most important factors determining the diagnostic quality. Since patient positioning is difficult and not standardized, an automated AI-based approach using depth images to automatically assess the patient's pose before the radiograph has been taken would be helpful. Due to regulatory hurdles, however, it is difficult in practice to acquire the required depth images and corresponding radiographs. In this paper, we present a framework that can generate such training data synthetically from Computer Tomography scans. We further show that by pretraining on our generated synthetic dataset consisting of 3077 image pairs of upper ankle joints, the pose assessment of real upper ankle joints can be improved by up to 11 percentage points.
Cite
Text
Laufer et al. "Synthetic Data Generated from CT Scans for Patient Pose Assessment." Medical Imaging with Deep Learning, 2025.Markdown
[Laufer et al. "Synthetic Data Generated from CT Scans for Patient Pose Assessment." Medical Imaging with Deep Learning, 2025.](https://mlanthology.org/midl/2025/laufer2025midl-synthetic/)BibTeX
@inproceedings{laufer2025midl-synthetic,
title = {{Synthetic Data Generated from CT Scans for Patient Pose Assessment}},
author = {Laufer, Manuel and Mairhöfer, Dominik and Sieren, Malte and Gerdes, Hauke and dos Reis, Fabio Leal and Bischof, Arpad and Käster, Thomas and Barth, Erhardt and Barkhausen, Jörg and Martinetz, Thomas},
booktitle = {Medical Imaging with Deep Learning},
year = {2025},
url = {https://mlanthology.org/midl/2025/laufer2025midl-synthetic/}
}