Label-free multimodal multiphoton imaging with deep learning for cancer detection and diagnosis

Stephen A. Boppart

doi:10.1117/12.2585139

5 March 2021 Label-free multimodal multiphoton imaging with deep learning for cancer detection and diagnosis

Stephen A. Boppart

Author Affiliations +

Proceedings Volume 11634, Multimodal Biomedical Imaging XVI; 116340C (2021) https://doi.org/10.1117/12.2585139
Event: SPIE BiOS, 2021, Online Only

Abstract

Label-free multimodal nonlinear optical imaging and optical biopsies of fresh, unstained, resected tissue specimens offer a wealth of new biomarkers for assessing the tumor microenvironment and diagnosing disease. By developing widely coherent supercontinuum from photonic crystal fibers, new excitation wavelengths can be generated to tailor the light stimulus in new ways. As a result, Simultaneous Label-free Auto-fluorescence Multi-harmonic (SLAM) microscopy can visualize the rich intrinsic molecular, metabolic, and structural information in cells and tissues. Results suggest broad potential of this stain-free, slide-free, imaging technology and methodology for real-time point-of-procedure applications, including the histopathological assessment of living biopsy specimens.

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Stephen A. Boppart "Label-free multimodal multiphoton imaging with deep learning for cancer detection and diagnosis", Proc. SPIE 11634, Multimodal Biomedical Imaging XVI, 116340C (5 March 2021); https://doi.org/10.1117/12.2585139

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