Synthetic Data Generated from CT Scans for Patient Pose Assessment

Manuel Laufer, Dominik Mairhöfer, Malte Sieren, Hauke Gerdes, Fabio Leal dos Reis, Arpad Bischof, Thomas Käster, Erhardt Barth, Jörg Barkhausen, Thomas Martinetz

Keywords: patient pose assessment, synthetic data generation, diagnostic quality, CT scan, time-of-flight cameras, radiography, deep learning

TL;DR: We present a framework that generates synthetic depth images and radiographs from CT scans that can be used to train Neural Networks which can assist staff in positioning patients in the X-ray process by assessing the quality of the patient´s pose.

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.

Primary Subject Area: Image Synthesis

Secondary Subject Area: Application: Radiology

Paper Type: Both

Registration Requirement: Yes

Submission Number: 181