SignificanceOral 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.AimA DL-enabled fluorescence spatial frequency domain imaging (SFDI) system trained with in silico tumor models was developed to quantify the depth of oral tumors.ApproachA 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.ResultsThe 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.ConclusionsA DL-enabled SFDI system trained with in silico CSH demonstrates promise in defining the deep margins of oral tumors.
KEYWORDS: Fluorescence tomography, Cancer, Surgery, Luminescence, Spatial frequencies, Monte Carlo methods, Imaging systems, Animal model studies, Tumors, Data modeling
Fluorescence imaging during to oral cancer surgery is typically 2D, yielding limited information on tumor depth. Here, we continue the development of a spatial frequency domain imaging (SFDI) system for 3D fluorescence imaging. A deep convolutional neural network takes as inputs SFDI-computed absorption, scattering and spatial-frequency fluorescence images, and yields images of fluorescence concentration and tumour depth. The model is trained using in silico data from Monte Carlo simulations of geometric tumor shapes (e.g., cylinder, spherical harmonics). Initial results yield average depth errors of <0.1 mm. Experiments are conducted in agar phantoms based on patient imaging.
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