Journal of biomedical optics · 2024
Deep learning-enabled fluorescence imaging for surgical guidance: in silico training for oral cancer depth quantification.
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Won Natalie J, Bartling Mandolin, La Macchia Josephine, Markevich Stefanie, Holtshousen Scott, Jagota Arjun, Negus Christina, Najjar Esmat, Wilson Brian C, Irish Jonathan C, Daly Michael J
Аннотация
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 μ g / 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.
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 μ g / 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.