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7 March 2023 Advancements in dynamic micro-optical coherence tomography for cell phenotyping
Hui Min Leung, Linhui Yu, Zhewei Wang, Zuzana Tatarova, Miquela O. Murray, Anagha Arvind, Amilcar Barrios, Estelle Danielle Sylvie Chiavassa, Sydney Kutowy, Hinnerk Schulz-Hildebrandt, Abigail L. Gregg, Joseph A. Gardecki, Oliver H. Jonas, Markus D. Herrmann, Guillermo J Tearney
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Proceedings Volume PC12378, Dynamics and Fluctuations in Biomedical Photonics XX; PC1237801 (2023) https://doi.org/10.1117/12.2649046
Event: SPIE BiOS, 2023, San Francisco, California, United States
Abstract
Dynamic micro-optical coherence tomography (DµOCT) is a technology that is capable of interrogating intracellular dynamics in intact, viable tissues. Towards our goal of advancing DµOCT for phenotyping cells, we imaged freshly excised human biopsies and performed correlative studies with histological results. To date, more than 30 biopsies from 17 patients with numerous types of gastrointestinal pathologies, including cancer, diverticulitis, and Crohn’s disease were imaged. In addition, using mouse models, we performed DµOCT imaging studies on tumors locally treated with chemotherapeutics delivered via custom implantable microdevices to observe the impact of those drugs on the tumor. Cyclical immunofluorescence staining was used to co-register ~20 markers on the same cross-sectional plane. We further demonstrate the utility of principal component analysis, K-means clustering, and convolutional long short-term memory (ConvLSTM) neural network for expanding the capabilities of DµOCT.
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Hui Min Leung, Linhui Yu, Zhewei Wang, Zuzana Tatarova, Miquela O. Murray, Anagha Arvind, Amilcar Barrios, Estelle Danielle Sylvie Chiavassa, Sydney Kutowy, Hinnerk Schulz-Hildebrandt, Abigail L. Gregg, Joseph A. Gardecki, Oliver H. Jonas, Markus D. Herrmann, and Guillermo J Tearney "Advancements in dynamic micro-optical coherence tomography for cell phenotyping", Proc. SPIE PC12378, Dynamics and Fluctuations in Biomedical Photonics XX, PC1237801 (7 March 2023); https://doi.org/10.1117/12.2649046
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KEYWORDS
Tomography
Tissues
Machine learning
Tumors
Feature extraction
Lymphatic system
Mouse models
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