Training Deep Learning Models for 2D Spine X-rays Using Synthetic Images and Annotations Created from 3D CT Volumes - Sukesh, Richin; Fieselmann, Andreas; Jaganathan, Srikrishna; Shetty, Karthik; Kaergel, Rainer; Kordon, Florian; Kappler, Steffen; Maier, Andreas (Bildverarbeitung für die Medizin 2022, 2022)
When training deep learning models in the medical domain, one is always burdened with the task of obtaining reliable medical data annotated by experts. However, the availability of annotated data is often limited. To overcome such limitations, this paper addresses the idea of using synthetic spine X-ray data to train a deep learning model to aid in the detection of vertebrae. For this purpose, a pipeline for automatic generation of synthetic datasets comprising synthetic Xray images and their corresponding annotations is developed and evaluated. The results of these experiments show improvements in detection rates of the model when synthetic X-ray data is added to the training dataset.<br
Keywords: X-ray Imaging; Spine; Object Detection; Synthetic Data