Tumor detection of the thyroid and salivary glands using hyperspectral imaging and deep learning

Abstract

The performance of hyperspectral imaging (HSI) for tumor detection is investigated in <i>ex-vivo</i> specimens from the thyroid (<i>N</i> = 200) and salivary glands (<i>N</i> = 16) from 82 patients. Tissues were imaged with HSI in broadband reflectance and autofluorescence modes. For comparison, the tissues were imaged with two fluorescent dyes. Additionally, HSI was used to synthesize three-band RGB multiplex images to represent the human-eye response and Gaussian RGBs, which are referred to as HSI-synthesized RGB images. Using histological ground truths, deep learning algorithms were developed for tumor detection. For the classification of thyroid tumors, HSI-synthesized RGB images achieved the best performance with an AUC score of 0.90. In salivary glands, HSI had the best performance with 0.92 AUC score. This study demonstrates that HSI could aid surgeons and pathologists in detecting tumors of the thyroid and salivary glands.