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13 March 2024 Convolutional neural networks estimate strain in quasistatic optical coherence elastography
Achuth Nair, Manmohan Singh, Salavat Aglyamov, Kirill Larin
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Proceedings Volume PC12844, Optical Elastography and Tissue Biomechanics XI; PC1284404 (2024) https://doi.org/10.1117/12.3001717
Event: SPIE BiOS, 2024, San Francisco, California, United States
Abstract
Assessing the biomechanical properties of tissues can have important applications for disease diagnosis and treatment monitoring. Optical coherence elastography is an established technology for measuring the biomechanical properties of tissues, and has been implemented ex vivo, in vivo, and clinically. Various steps are required for effective translation of this technology, including making improvements in data analysis and processing. OCE data can be inherently noisy, a single data acquisition can consist of gigabytes of data, and data processing can be lengthy. In this work, we examine how convolutional neural networks can be implemented to efficiently process OCE data.
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Achuth Nair, Manmohan Singh, Salavat Aglyamov, and Kirill Larin "Convolutional neural networks estimate strain in quasistatic optical coherence elastography", Proc. SPIE PC12844, Optical Elastography and Tissue Biomechanics XI, PC1284404 (13 March 2024); https://doi.org/10.1117/12.3001717
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KEYWORDS
Convolutional neural networks
Elastography
Optical coherence
Biomechanics
Biomedical applications
Data processing
Nervous system
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