Training Deep Learning Models for 2D Spine X-rays Using Synthetic Images and Annotations Created from 3D CT Volumes

Sukesh R, Fieselmann A, Jaganathan S, Shetty K, Kaergel R, Kordon F, Kappler S, Maier A (2022)

Publication Language: English

Publication Type: Conference contribution

Publication year: 2022

Publisher: Springer Fachmedien Wiesbaden

City/Town: Wiesbaden

Pages Range: 63--68

Conference Proceedings Title: Bildverarbeitung für die Medizin 2022

Event location: Heidelberg

ISBN: 978-3-658-36932-3

DOI: 10.1007/978-3-658-36932-3_13

Abstract

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.

Authors with CRIS profile

Additional Organisation(s)

Involved external institutions

Siemens AG Germany (DE)

How to cite

APA:

Sukesh, R., Fieselmann, A., Jaganathan, S., Shetty, K., Kaergel, R., Kordon, F.,... Maier, A. (2022). Training Deep Learning Models for 2D Spine X-rays Using Synthetic Images and Annotations Created from 3D CT Volumes. In Maier-Hein K, Deserno TM, Handels H, Maier A, Palm C, Tolxdorff T (Eds.), Bildverarbeitung für die Medizin 2022 (pp. 63--68). Heidelberg, DE: Wiesbaden: Springer Fachmedien Wiesbaden.

MLA:

Sukesh, Richin, et al. "Training Deep Learning Models for 2D Spine X-rays Using Synthetic Images and Annotations Created from 3D CT Volumes." Proceedings of the Bildverarbeitung für die Medizin 2022, Heidelberg Ed. Maier-Hein K, Deserno TM, Handels H, Maier A, Palm C, Tolxdorff T, Wiesbaden: Springer Fachmedien Wiesbaden, 2022. 63--68.

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