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1. Prevalence and practices of immunofluorescent cell image processing: a systematic review

   https://doi.org/10.3389/fncel.2023.1188858  

   Helmbrecht, Hawley; Lin, Teng-Jui; Janakiraman, Sanjana; Decker, Kaleb; Nance, Elizabeth (July 2023, Frontiers in Cellular Neuroscience)

   BackgroundWe performed a systematic review that identified at least 9,000 scientific papers on PubMed that include immunofluorescent images of cells from the central nervous system (CNS). These CNS papers contain tens of thousands of immunofluorescent neural images supporting the findings of over 50,000 associated researchers. While many existing reviews discuss different aspects of immunofluorescent microscopy, such as image acquisition and staining protocols, few papers discuss immunofluorescent imaging from an image-processing perspective. We analyzed the literature to determine the image processing methods that were commonly published alongside the associated CNS cell, microscopy technique, and animal model, and highlight gaps in image processing documentation and reporting in the CNS research field. MethodsWe completed a comprehensive search of PubMed publications using Medical Subject Headings (MeSH) terms and other general search terms for CNS cells and common fluorescent microscopy techniques. Publications were found on PubMed using a combination of column description terms and row description terms. We manually tagged the comma-separated values file (CSV) metadata of each publication with the following categories: animal or cell model, quantified features, threshold techniques, segmentation techniques, and image processing software. ResultsOf the almost 9,000 immunofluorescent imaging papers identified in our search, only 856 explicitly include image processing information. Moreover, hundreds of the 856 papers are missing thresholding, segmentation, and morphological feature details necessary for explainable, unbiased, and reproducible results. In our assessment of the literature, we visualized current image processing practices, compiled the image processing options from the top twelve software programs, and designed a road map to enhance image processing. We determined that thresholding and segmentation methods were often left out of publications and underreported or underutilized for quantifying CNS cell research. DiscussionLess than 10% of papers with immunofluorescent images include image processing in their methods. A few authors are implementing advanced methods in image analysis to quantify over 40 different CNS cell features, which can provide quantitative insights in CNS cell features that will advance CNS research. However, our review puts forward that image analysis methods will remain limited in rigor and reproducibility without more rigorous and detailed reporting of image processing methods. ConclusionImage processing is a critical part of CNS research that must be improved to increase scientific insight, explainability, reproducibility, and rigor. 

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2. Imaging tissues and cells beyond the diffraction limit with structured illumination microscopy and Bayesian image reconstruction

   https://doi.org/doi: 10.1093/gigascience/giy126  

   Pospisil, J; Lukes, T; Bendesky, J; Fliegel, K; Spendier, K; Hagen, G (January 2019, Gigascience)

   Background Structured illumination microscopy (SIM) is a family of methods in optical fluorescence microscopy that can achieve both optical sectioning and super-resolution effects. SIM is a valuable method for high-resolution imaging of fixed cells or tissues labeled with conventional fluorophores, as well as for imaging the dynamics of live cells expressing fluorescent protein constructs. In SIM, one acquires a set of images with shifting illumination patterns. This set of images is subsequently treated with image analysis algorithms to produce an image with reduced out-of-focus light (optical sectioning) and/or with improved resolution (super-resolution). Findings Five complete, freely available SIM datasets are presented including raw and analyzed data. We report methods for image acquisition and analysis using open-source software along with examples of the resulting images when processed with different methods. We processed the data using established optical sectioning SIM and super-resolution SIM methods and with newer Bayesian restoration approaches that we are developing. Conclusions Various methods for SIM data acquisition and processing are actively being developed, but complete raw data from SIM experiments are not typically published. Publically available, high-quality raw data with examples of processed results will aid researchers when developing new methods in SIM. Biologists will also find interest in the high-resolution images of animal tissues and cells we acquired. All of the data were processed with SIMToolbox, an open-source and freely available software solution for SIM. 

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3. Spatial ecology of microbial colonization intensity and behavior in rice rhizoplane biofilms analyzed by CMEIAS Bioimage Informatics software

   Dazzo, F.B.; Yanni, Y.G. (August 2017, Journal of soil science and plant nutrition)

   CMEIAS (Center for Microbial Ecology Image Analysis System) Bioimage Informatics software is designed to strengthen microscopy-based approaches for understanding microbial ecology at spatial scales directly relevant to ecological functions performed by individual cells and microcolonies. Copyrighted software components are thoroughly documented and provided as free downloads at . The software tools already released include CMEIAS-Image Tool v. 1.28, CMEIAS Color Segmentation, CMEIAS Quadrat Maker and CMEIAS JFrad Fractal Dimension analysis. The spatial ecology module of the next CMEIAS upgrade currently being developed (version 4.0) is designed to extract data from images for analysis of plotless point-patterns, quadrat-lattice patterns, geostatistical autocorrelation and fractal geometry of cells within biofilms. Examples presented here illustrate how selected CMEIAS attributes can be used to analyze the in situ spatial intensity, pattern of distribution, and colonization behavior of an indigenous population of a rhizobial strain on a sampled image of the rhizoplane landscape of a rice plant grown in field soil. The spatial ecology information gained can provide useful insights that help to predict the most likely performance of the biofertilizer test strain in relation to the growth response of the crop under field conditions. 

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4. Rotation equivariant and invariant neural networks for microscopy image analysis

   https://doi.org/10.1093/bioinformatics/btz353  

   Chidester, Benjamin; Zhou, Tianming; Do, Minh_N; Ma, Jian (July 2019, Bioinformatics)

   Abstract MotivationNeural networks have been widely used to analyze high-throughput microscopy images. However, the performance of neural networks can be significantly improved by encoding known invariance for particular tasks. Highly relevant to the goal of automated cell phenotyping from microscopy image data is rotation invariance. Here we consider the application of two schemes for encoding rotation equivariance and invariance in a convolutional neural network, namely, the group-equivariant CNN (G-CNN), and a new architecture with simple, efficient conic convolution, for classifying microscopy images. We additionally integrate the 2D-discrete-Fourier transform (2D-DFT) as an effective means for encoding global rotational invariance. We call our new method the Conic Convolution and DFT Network (CFNet). ResultsWe evaluated the efficacy of CFNet and G-CNN as compared to a standard CNN for several different image classification tasks, including simulated and real microscopy images of subcellular protein localization, and demonstrated improved performance. We believe CFNet has the potential to improve many high-throughput microscopy image analysis applications. Availability and implementationSource code of CFNet is available at: https://github.com/bchidest/CFNet. Supplementary informationSupplementary data are available at Bioinformatics online. 

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5. Semi-supervised machine learning workflow for analysis of nanowire morphologies from transmission electron microscopy images

   https://doi.org/10.1039/D2DD00066K  

   Lu, Shizhao; Montz, Brian; Emrick, Todd; Jayaraman, Arthi (December 2022, Digital Discovery)

   In the field of materials science, microscopy is the first and often only accessible method for structural characterization. There is a growing interest in the development of machine learning methods that can automate the analysis and interpretation of microscopy images. Typically training of machine learning models requires large numbers of images with associated structural labels, however, manual labeling of images requires domain knowledge and is prone to human error and subjectivity. To overcome these limitations, we present a semi-supervised transfer learning approach that uses a small number of labeled microscopy images for training and performs as effectively as methods trained on significantly larger image datasets. Specifically, we train an image encoder with unlabeled images using self-supervised learning methods and use that encoder for transfer learning of different downstream image tasks (classification and segmentation) with a minimal number of labeled images for training. We test the transfer learning ability of two self-supervised learning methods: SimCLR and Barlow-Twins on transmission electron microscopy (TEM) images. We demonstrate in detail how this machine learning workflow applied to TEM images of protein nanowires enables automated classification of nanowire morphologies ( e.g. , single nanowires, nanowire bundles, phase separated) as well as segmentation tasks that can serve as groundwork for quantification of nanowire domain sizes and shape analysis. We also extend the application of the machine learning workflow to classification of nanoparticle morphologies and identification of different type of viruses from TEM images. 

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