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

Hinano Imanishi; Takahiro Nishimura; Yu Shimojo; Kunio Awazu

doi:10.1364/BOE.500022

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

Biomed Opt Express. 2023 Sep 18;14(10):5254-5266. doi: 10.1364/BOE.500022. eCollection 2023 Oct 1.

Authors

Hinano Imanishi¹, Takahiro Nishimura¹, Yu Shimojo^{1

2

3}, Kunio Awazu^{1

4}

Affiliations

¹ Graduate School of Engineering, Osaka University, Yamadaoka 2-1, Suita, Osaka 565-0871, Japan.
² Graduate School of Medicine, Osaka Metropolitan University, Asahimachi 1-4-3, Abeno-ku, Osaka 545-8585, Japan.
³ Research Fellow of Japan Society for the Promotion of Science, Kojimachi 5-3-1, Chiyoda-ku, Tokyo 102-0083, Japan.
⁴ Global Center for Medical Engineering and Informatics, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan.

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.