Summary In Life Science research, electron microscopy (EM) is an essential tool for morphological analysis at the subcellular level as it allows for visualization at nanometer resolution. However, electron micrographs contain image degradations such as noise and blur caused by electromagnetic interference, electron counting errors, magnetic lens imperfections, electron diffraction, etc. These imperfections in raw image quality are inevitable and hamper subsequent image analysis and visualization. In an effort to mitigate these artefacts, many electron microscopy image restoration algorithms have been proposed in the last years. Most of these methods rely on generic assumptions on the image or degradations and are therefore outperformed by advanced methods that are based on more accurate models. Ideally, a method will accurately model the specific degradations that fit the physical acquisition settings. In this overview paper, we discuss different electron microscopy image degradation solutions and demonstrate that dedicated artefact regularisation results in higher quality restoration and is applicable through recently developed probabilistic methods.
Microscopy is one of the most essential imaging techniques in life sciences. High-quality images are required in order to solve (potentially life-saving) biomedical research problems. Many microscopy techniques do not achieve sufficient resolution for these purposes, being limited by physical diffraction and hardware deficiencies. Electron microscopy addresses optical diffraction by measuring emitted or transmitted electrons instead of photons, yielding nanometer resolution. Despite pushing back the diffraction limit, blur should still be taken into account because of practical hardware imperfections and remaining electron diffraction. Deconvolution algorithms can remove some of the blur in post-processing but they depend on knowledge of the point-spread function (PSF) and should accurately regularize noise. Any errors in the estimated PSF or noise model will reduce their effectiveness. This paper proposes a new procedure to estimate the lateral component of the point spread function of a 3D scanning electron microscope more accurately. We also propose a Bayesian maximum a posteriori deconvolution algorithm with a non-local image prior which employs this PSF estimate and previously developed noise statistics. We demonstrate visual quality improvements and show that applying our method improves the quality of subsequent segmentation steps.
The integrity of ER–mitochondria appositions ensures transfer of ions and phospholipids (PLs) between these organelles and exerts crucial effects on mitochondrial bioenergetics. Malfunctions within the ER–mitochondria contacts altering lipid trafficking homeostasis manifest in diverse pathologies, but the molecular effectors governing this process remain ill-defined. Here, we report that PERK promotes lipid trafficking at the ER–mitochondria contact sites (EMCS) through a non-conventional, unfolded protein response-independent, mechanism. PERK operates as an adaptor for the recruitment of the ER–plasma membrane tether and lipid transfer protein (LTP) Extended-Synaptotagmin 1 (E-Syt1), within the EMCS. In resting cells, the heterotypic E-Syt1-PERK interaction endorses transfer of PLs between the ER and mitochondria. Weakening the E-Syt1-PERK interaction or removing the lipid transfer SMP-domain of E-Syt1, compromises mitochondrial respiration. Our findings unravel E-Syt1 as a PERK interacting LTP and molecular component of the lipid trafficking machinery of the EMCS, which critically maintains mitochondrial homeostasis and fitness.
Sewage sludge contains a significant concentration of sulphur (0.3–2.3 wt%) that hampers sludge (co-)combustion as the final disposal route due to the formation and emission of gaseous SO2 and associated acid rain problems. It is therefore of paramount importance to limit the sulphur content of waste-activated sludge (WAS) as much as possible. This paper studies the distribution and transformations of sulphur compounds occurring during the successive steps in the sewage sludge treatment. Data were gathered from sampling and analyzing all relevant water and sludge streams of a full-scale treatment plant during 1 month. It was seen that sulphates are the predominant compounds in secondary sludge. During thickening, the oxygen level in the sludge decreases due to microbial activity, and the sulphates are gradually transformed into sulphides. The process continues when thickened sludge is stored in the sludge storage tanks. After anaerobic digestion, the ORP of the sludge has decreased to such an extent that all inorganic sulphur is transformed into sulphides. The experimental results were confirmed by a chemical speciation. To limit the residual sulphur content in the sludge, it is hence beneficial to dewater the sludge when still in aerobic conditions, thus largely releasing soluble sulphates with the sludge water.
Over the last decade, electron microscopy has improved up to a point that generating high quality gigavoxel sized datasets only requires a few hours. Automated image analysis, particularly image segmentation, however, has not evolved at the same pace. Even though state-of-the-art methods such as U-Net and DeepLab have improved segmentation performance substantially, the required amount of labels remains too expensive. Active learning is the subfield in machine learning that aims to mitigate this burden by selecting the samples that require labeling in a smart way. Many techniques have been proposed, particularly for image classification, to increase the steepness of learning curves. In this work, we extend these techniques to deep CNN based image segmentation. Our experiments on three different electron microscopy datasets show that active learning can improve segmentation quality by 10 to 15% in terms of Jaccard score compared to standard randomized sampling.
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.
