Deep learning architectures for spatial-frequency 3D fluorescence in oral cancer surgery models

Natalie J. Won; Anjolaluawa Adewale; Jerry Wan; Mandolin Bartling; Jason Townson; Harley Chan; Esmat Najjar; Alon Pener-Tessler; Brian C. Wilson; Jonathan C. Irish; Michael J. Daly

doi:10.1117/12.3002880

12 March 2024 Deep learning architectures for spatial-frequency 3D fluorescence in oral cancer surgery models

Natalie J. Won, Anjolaluawa Adewale, Jerry Wan, Mandolin Bartling, Jason Townson, Harley Chan, Esmat Najjar, Alon Pener-Tessler, Brian C. Wilson, Jonathan C. Irish, Michael J. Daly

Author Affiliations +

Proceedings Volume 12825, Molecular-Guided Surgery: Molecules, Devices, and Applications X; 128250D (2024) https://doi.org/10.1117/12.3002880
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

Fluorescence-guided surgery systems employed during oral cancer resection help detect the lateral margin yet fail to quantify the deep margins of the tumor prior to resection. Without comprehensive quantification of three-dimensional tumor margins, complete resection remains challenging. While interoperative techniques to assess the deep margin exist, they are limited in precision, leaving an unmet need for a system that can quantify depth. Our group is developing a deep learning (DL)-enabled fluorescence spatial frequency domain imaging (SFDI) system to address this limitation. The SFDI system captures fluorescence (F) and reflectance (R) images that contain information on tissue optical properties (OP) and depth sensitivity across spatial frequencies. Coupling DL with SFDI imaging allows for the near-real time construction of depth and concentration maps. Here, we compare three DL architectures that use SFDI images as inputs: i) F+OP, where OP (absorption and scattering) are obtained analytically from reflectance images; ii) F+R; iii) F/R. Training the three models required 10,000 tumor samples; synthetic tumors derived from composite spherical harmonics circumvented the need for patient data. The synthetic tumors were passed to a diffusion-theory light propagation model to generate a dataset of artificial SFDI images for DL training. Two oral cancer models derived from MRI of patient tongue tumors are used to evaluate DL performance in: i) in silico SFDI images ii) optical phantoms. These studies evaluate how system performance is affected by the SFDI input data and DL architectures. Future studies are required to assess system performance in vivo.

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Natalie J. Won, Anjolaluawa Adewale, Jerry Wan, Mandolin Bartling, Jason Townson, Harley Chan, Esmat Najjar, Alon Pener-Tessler, Brian C. Wilson, Jonathan C. Irish, and Michael J. Daly "Deep learning architectures for spatial-frequency 3D fluorescence in oral cancer surgery models", Proc. SPIE 12825, Molecular-Guided Surgery: Molecules, Devices, and Applications X, 128250D (12 March 2024); https://doi.org/10.1117/12.3002880

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