Head and neck cancer visualization using deep learning combined with fluorescence lifetime imaging and white light imaging

Takanori Fukazawa; Mark Marsden; Brent W. Weyers; Yu-Cheng Deng; Julien Bec; D. Gregory Farwell; Laura Marcu

doi:10.1117/12.2581449

5 March 2021 Head and neck cancer visualization using deep learning combined with fluorescence lifetime imaging and white light imaging

Takanori Fukazawa, Mark Marsden, Brent W. Weyers, Yu-Cheng Deng, Julien Bec, D. Gregory Farwell, Laura Marcu

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Proceedings Volume 11631, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XIX; 116310O (2021) https://doi.org/10.1117/12.2581449
Event: SPIE BiOS, 2021, Online Only

Abstract

A key step for mitigating tumor recurrence for patients with head and neck cancer is adequate surgical margin delineation. Presently available techniques however limit accurate tumor margin detection during surgery. Herein, we report on tumor visualization using deep learning by combining autofluorescence images acquired by a fiber-based fluorescence lifetime imaging (FLIm) system and white light images (WLI) obtained by surgical cameras. To accomplish accurate registration between FLIm and WLI, a tissue motion correction algorithm was employed as a pre-processing step. The trained model was applied to differentiation of healthy and cancerous tissues in a 50 head and neck cancer patients dataset (ROC-AUC : 0.87).

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Takanori Fukazawa, Mark Marsden, Brent W. Weyers, Yu-Cheng Deng, Julien Bec, D. Gregory Farwell, and Laura Marcu "Head and neck cancer visualization using deep learning combined with fluorescence lifetime imaging and white light imaging", Proc. SPIE 11631, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XIX, 116310O (5 March 2021); https://doi.org/10.1117/12.2581449

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