Spatial and spectral optimization of two-photon imaging data for optimal label-free texture-based tissue classification models (Conference Presentation)

Thomas G. Knapp; Suzann Duan; Juanita L. Merchant; Travis W. Sawyer

doi:10.1117/12.2650142

16 March 2023 Spatial and spectral optimization of two-photon imaging data for optimal label-free texture-based tissue classification models (Conference Presentation)

Thomas G. Knapp, Suzann Duan, Juanita L. Merchant, Travis W. Sawyer

Author Affiliations +

Proceedings Volume PC12391, Label-free Biomedical Imaging and Sensing (LBIS) 2023; PC123910A (2023) https://doi.org/10.1117/12.2650142
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

Multiphoton microscopy was used to collect five spectral channels of two-photon excited fluorescence (2PEF) from clinical duodenal neuroendocrine tumor specimens. As clinical use of a device capable of collecting 2PEF images would prohibit such extensive data collection, the optimal spatial and spectral data required for tissue classification was determined. Tissue classifiers were created using linear discriminant analysis of texture feature data from 2PEF images. Classifier accuracy, and the features that provided the highest accuracy, were compared at varying stages of resolution downsampling. Results suggest optimal imaging parameters for a system designed for the automatic detection of cancerous lesions using 2PEF.

Conference Presentation

Citation Download Citation

Thomas G. Knapp, Suzann Duan, Juanita L. Merchant, and Travis W. Sawyer "Spatial and spectral optimization of two-photon imaging data for optimal label-free texture-based tissue classification models (Conference Presentation)", Proc. SPIE PC12391, Label-free Biomedical Imaging and Sensing (LBIS) 2023, PC123910A (16 March 2023); https://doi.org/10.1117/12.2650142

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