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18 September 2024 Deep learning–enabled fluorescence imaging for surgical guidance: in silico training for oral cancer depth quantification
Natalie J. Won, Mandolin Bartling, Josephine La Macchia, Stefanie Markevich, Scott Holtshousen, Arjun Jagota, Christina Negus, Esmat Najjar, Brian C. Wilson, Jonathan C. Irish, Michael J. Daly
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Journal of Biomedical Optics, Vol. 30, Issue S1, S13706 (September 2024). https://doi.org/10.1117/1.JBO.30.S1.S13706
Abstract

Significance

Oral cancer surgery requires accurate margin delineation to balance complete resection with post-operative functionality. Current in vivo fluorescence imaging systems provide two-dimensional margin assessment yet fail to quantify tumor depth prior to resection. Harnessing structured light in combination with deep learning (DL) may provide near real-time three-dimensional margin detection.

Aim

A DL-enabled fluorescence spatial frequency domain imaging (SFDI) system trained with in silico tumor models was developed to quantify the depth of oral tumors.

Approach

A convolutional neural network was designed to produce tumor depth and concentration maps from SFDI images. Three in silico representations of oral cancer lesions were developed to train the DL architecture: cylinders, spherical harmonics, and composite spherical harmonics (CSHs). Each model was validated with in silico SFDI images of patient-derived tongue tumors, and the CSH model was further validated with optical phantoms.

Results

The performance of the CSH model was superior when presented with patient-derived tumors (P-value<0.05). The CSH model could predict depth and concentration within 0.4 mm and 0.4 μg/mL, respectively, for in silico tumors with depths less than 10 mm.

Conclusions

A DL-enabled SFDI system trained with in silico CSH demonstrates promise in defining the deep margins of oral tumors.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Natalie J. Won, Mandolin Bartling, Josephine La Macchia, Stefanie Markevich, Scott Holtshousen, Arjun Jagota, Christina Negus, Esmat Najjar, Brian C. Wilson, Jonathan C. Irish, and Michael J. Daly "Deep learning–enabled fluorescence imaging for surgical guidance: in silico training for oral cancer depth quantification," Journal of Biomedical Optics 30(S1), S13706 (18 September 2024). https://doi.org/10.1117/1.JBO.30.S1.S13706
Received: 30 April 2024; Accepted: 29 August 2024; Published: 18 September 2024
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KEYWORDS
Tumors
Education and training
Fluorescence
Data modeling
Cancer
Fluorescence imaging
Spherical harmonics
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