Deep learning based depth map estimation of protoporphyrin IX in turbid media using dual wavelength excitation fluorescence

Abstract

This study presents a depth map estimation of fluorescent objects in turbid media, such as biological tissue based on fluorescence observation by two-wavelength excitation and deep learning-based processing. A U-Net-based convolutional neural network is adapted for fluorophore depth maps from the ratiometric information of the two-wavelength excitation fluorescence. The proposed method offers depth map estimation from wide-field fluorescence images with rapid processing. The feasibility of the proposed method was demonstrated experimentally by estimating the depth map of protoporphyrin IX, a recognized cancer biomarker, at different depths within an optical phantom.