Deep-Learning High-Dynamic-Range Ultrasound
Prado E, Pavlov I, Navab N, Zahnd G (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: IEEE Computer Society
Book Volume: 2019-October
Pages Range: 479-482
Conference Proceedings Title: IEEE International Ultrasonics Symposium, IUS
Event location: Glasgow, GBR
ISBN: 9781728145969
DOI: 10.1109/ULTSYM.2019.8925685
Abstract
In recent years ultrasound imaging has achieved an increasing acceptance across medical specialties. For this reason new techniques keep being tested in the field. Among these techniques we found High Dynamic Range (HDR) imaging where the range of luminosity levels is augmented by combining multiple expositions of a scene. Current ultrasound techniques present limitations that are not compatible with traditional implementations of HDR imaging. In this paper, we asses the use of a deep learning (DL) neural network (U-net architecture) on predicting HDR values from low dynamic range (LDR) input images. In addition, an image acquisition pipeline to create the data set from which the network was trained is described. We demonstrated that this type of networks can be trained to predict HDR out from a minimal number of input expositions, while the obtained results showed to be comparable with more traditional approaches.
Involved external institutions
Germany (DE)How to cite
APA:
Prado, E., Pavlov, I., Navab, N., & Zahnd, G. (2019). Deep-Learning High-Dynamic-Range Ultrasound. In IEEE International Ultrasonics Symposium, IUS (pp. 479-482). Glasgow, GBR: IEEE Computer Society.
MLA:
Prado, Eduardo, et al. "Deep-Learning High-Dynamic-Range Ultrasound." Proceedings of the 2019 IEEE International Ultrasonics Symposium, IUS 2019, Glasgow, GBR IEEE Computer Society, 2019. 479-482.
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