We believe that an interactive approach is required as the restored image data can only be validated by experts. Even though most of these methods are available to the community, they are often impractical due to low-level programming environments, parameter sensitivity, and high computational demands. Many of these methods have shown remarkable performance for 3D EM applications 20, 21, 22, 23, 24, 25. State-of-the-art denoising methods are based on multiresolution shrinkage 14, 15, nonlocal pixel averaging 15, 16, Bayesian estimation 17, 18, or convolutional neural networks 19. However, the noise level increases as the dwell time decreases, which can introduce issues with regard to subsequent visualization, segmentation, and analysis of ultrastructure.įor the last few years, there has been great progress in computer vision research, particularly in image denoising, which aims to restore the true image signal from noisy data. Shorter dwell times have two advantages: shorter total acquisition time and less risk to overexposure artefacts such as charging. A potential solution arises in the dwell-time acquisition parameter, i.e., the time that is used to “illuminate” one pixel. Consequently, this approach is limited in terms of scalability. 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. 13, it still requires 6 months and six FIB-SEM machines to section an entire Drosophila ventral nerve cord of ~2.6 × 10 7 μm 3 voxels. Even considering the impressive tenfold speedup obtained by Xu et al. Recent ambitious research projects, such as imaging 10 7 μm 3 sections of Drosophila brain and mammalian neuronal tissue 12, 13 at 8 nm 3 isotropic resolution for connectomics research have taken volume EM imaging to a next level. The advantage of generating high-resolution 3D information, and also a comprehensive view of a complete cell or tissue, has invited the scientific community to apply these techniques for many different research questions. Over the past years, there has been a substantial increase in the use of these techniques in life science research 7, 8, 9, 10, 11, 12. While both SBF-SEM and FIB-SEM have the potential to generate images at 3- to 5-nm lateral resolution, the FIB milling is more precise than the mechanical SBF-SEM slicing, resulting in a maximal axial resolution of 5 and 20 nm, respectively 2, 5, 6. A similar slice-and-view approach is used in focused ion beam (FIB) SEM, where the block face is removed by FIB milling. Eventually, this results in a stack of 2D images that can be compiled to a high-resolution 3D volume image. SBF-SEM repetitively acquires a 2D SEM image from the smoothened sample surface (or block face) and then removes the top of the sample with a diamond knife ultramicrotome 3, 4, revealing the next sample surface to be imaged. The development of serial block face (SBF) scanning EM (SEM) techniques has made 3D EM more easily available for large-scale imaging of biological samples 2. Lots of progress has been made in this field by automating acquisition, which eventually enabled successful imaging of the complete Drosophila melanogaster brain at synaptic resolution 1. The classical setup typically involves serial sectioning and high-resolution imaging by transmission EM (TEM). The field of three-dimensional electron microscopy (3D EM) covers several technologies that unveil a sample at nanometer (nm) resolution. Lastly, we show that image denoising benefits visualization and (semi-)automated segmentation and analysis of ultrastructure in various volume EM datasets. 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. We present DenoisEM: an interactive and GPU accelerated denoising plugin for ImageJ that ensures fast parameter tuning and processing through parallel computing. 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. Moreover, large 3D EM datasets typically require hours to days to be acquired and accelerated imaging typically results in noisy data. This has caused an explosion in dataset size, necessitating the development of automated workflows. The recent advent of 3D in electron microscopy (EM) has allowed for detection of nanometer resolution structures.
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