Rethinking Ultrasound Augmentation: A Physics-Inspired Approach
Tirindelli M, Eilers C, Simson W, Paschali M, Azampour MF, Navab N (2021)
Publication Type: Conference contribution
Publication year: 2021
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12908 LNCS
Pages Range: 690-700
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Virtual, Online
ISBN: 9783030872366
DOI: 10.1007/978-3-030-87237-3_66
Abstract
Medical Ultrasound (US), despite its wide use, is characterized by artefacts and operator dependency. Those attributes hinder the gathering and utilization of US datasets for the training of deep neural networks used for computer-assisted intervention systems. Data augmentation is commonly used to enhance model generalization and performance. However, common data augmentation techniques, such as affine transformations do not align with the physics of US and, when used carelessly can lead to unrealistic US images. To this end, we propose a set of physics-inspired transformations, including deformation, reverb and signal-to-noise ratio, that we apply on US B-mode images for data augmentation. We evaluate our method on a new spine US dataset for the tasks of bone segmentation and classification.
Involved external institutions
Germany (DE)How to cite
APA:
Tirindelli, M., Eilers, C., Simson, W., Paschali, M., Azampour, M.F., & Navab, N. (2021). Rethinking Ultrasound Augmentation: A Physics-Inspired Approach. In Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 690-700). Virtual, Online: Springer Science and Business Media Deutschland GmbH.
MLA:
Tirindelli, Maria, et al. "Rethinking Ultrasound Augmentation: A Physics-Inspired Approach." Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online Ed. Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert, Springer Science and Business Media Deutschland GmbH, 2021. 690-700.
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