Denoising of stimulated Raman scattering microscopy images via deep learning.

Bryce Manifold,Andrew T Francis,Andrew H Hill,Elena Thomas,Dan Fu

doi:10.1364/boe.10.003860

Abstract

Stimulated Raman scattering (SRS) microscopy is a label-free quantitative chemical imaging technique that has demonstrated great utility in biomedical imaging applications ranging from real-time stain-free histopathology to live animal imaging. However, similar to many other nonlinear optical imaging techniques, SRS images often suffer from low signal to noise ratio (SNR) due to absorption and scattering of light in tissue as well as the limitation in applicable power to minimize photodamage. We present the use of a deep learning algorithm to significantly improve the SNR of SRS images. Our algorithm is based on a U-Net convolutional neural network (CNN) and significantly outperforms existing denoising algorithms. More importantly, we demonstrate that the trained denoising algorithm is applicable to images acquired at different zoom, imaging power, imaging depth, and imaging geometries that are not included in the training. Our results identify deep learning as a powerful denoising tool for biomedical imaging at large, with potential towards in vivo applications, where imaging parameters are often variable and ground-truth images are not available to create a fully supervised learning training set.

Highlights

Stimulated Raman scattering (SRS) microscopy is a powerful optical imaging technique that uses the intrinsic vibrational contrast of molecules to provide chemical maps of biological cells and tissues
Our findings demonstrate the power of convolutional neural network (CNN)-based deep learning as a denoising technique and provide an avenue to significantly improve the quality of biological images acquired in a wide variety of low signal to noise ratio (SNR) conditions
Because the SNR is linearly proportional to the Stokes power in SRS imaging [35], images acquired at 1 mW (Fig. 2(A)) display 20-fold lower SNR than those acquired at 20 mW (Fig. 2(D))

Summary

Introduction

Stimulated Raman scattering (SRS) microscopy is a powerful optical imaging technique that uses the intrinsic vibrational contrast of molecules to provide chemical maps of biological cells and tissues. Epi imaging acquires back-scattered light from the sample which yields significantly weaker signal in comparison to transmission mode detection These challenges (depth, laser power, and detection scheme) are common in biological imaging and often result in the acquisition of low SNR images. The fully-connected architecture of the most common CNNs for denoising involve significant training times and require large training samples to be effective These deep learning denoising algorithms are based on RGB images with relatively narrowband noise (noise centered around a small frequency range) [27]. Our findings demonstrate the power of CNN-based deep learning as a denoising technique and provide an avenue to significantly improve the quality of biological images acquired in a wide variety of low SNR conditions

Sample preparation

SRS imaging

Denoising low and high power SRS images of fixed HeLa cells

Denoising deep SRS images of ex vivo mouse brain

Discussion