An interactive ImageJ plugin for semi-automated image denoising in electron microscopy

Joris Roels,Yvan Saeys,Hiêp Q Luong,Saskia Lippens,Frank Vernaillen,Bart Goossens,Jan Aelterman,Wilfried Philips,Anna Kremer,Amanda Gonçalves

doi:10.1038/s41467-020-14529-0

Joris Roels, Yvan Saeys + Show 8 more

Open Access

https://doi.org/10.1038/s41467-020-14529-0

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Abstract

The recent advent of 3D in electron microscopy (EM) has allowed for detection of nanometer resolution structures. This has caused an explosion in dataset size, necessitating the development of automated workflows. Moreover, large 3D EM datasets typically require hours to days to be acquired and accelerated imaging typically results in noisy data. Advanced denoising techniques can alleviate this, but tend to be less accessible to the community due to low-level programming environments, complex parameter tuning or a computational bottleneck. We present DenoisEM: an interactive and GPU accelerated denoising plugin for ImageJ that ensures fast parameter tuning and processing through parallel computing. Experimental results show that DenoisEM is one order of magnitude faster than related software and can accelerate data acquisition by a factor of 4 without significantly affecting data quality. Lastly, we show that image denoising benefits visualization and (semi-)automated segmentation and analysis of ultrastructure in various volume EM datasets.

Highlights

The recent advent of 3D in electron microscopy (EM) has allowed for detection of nanometer resolution structures
Practical solutions that allow for user feedback to apply state-of-the-art denoising on large 3D EM data sets generated by e.g., scanning EM (SEM) or serial section transmission EM (TEM) are not readily available
We developed DenoisEM, a GPU accelerated denoising plugin with an interactive workflow that allows for efficient interaction and feedback by the expert

Summary

Introduction

The recent advent of 3D in electron microscopy (EM) has allowed for detection of nanometer resolution structures. SBF-SEM repetitively acquires a 2D SEM image from the smoothened sample surface (or block face) and removes the top of the sample with a diamond knife ultramicrotome[3,4], revealing the sample surface to be imaged This results in a stack of 2D images that can be compiled to a high-resolution 3D volume image. Note that the classical image acquisition setup with a single FIB-SEM machine, used by most other research facilities, would require more than 5 years This approach is limited in terms of scalability. Our plugin is publicly available at http://bioimagingcore.be/DenoisEM

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