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Copy file name to clipboardExpand all lines: _sources/authors/aastha_mathur.md
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Bioimaging has now entered the era of big data with faster than ever development of complex microscopy technologies leading to increasingly complex datasets. This enormous increase in data size and informational complexity within those datasets has brought with it several difficulties in terms of common and harmonized data handling, analysis and management practices, which are currently hampering the full potential of image data being realized. Here we outline a wide range of efforts and solutions currently being developed by the microscopy community to address these challenges on the path towards FAIR bioimage data. We also highlight how different actors in the microscopy ecosystem are working together, creating synergies that develop new approaches, and how research infrastructures, such as Euro-BioImaging, are fostering these interactions to shape the field.
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Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we take a first step at presenting some of the most common use cases as well as critical/emerging use cases of (including the use of artificial intelligence for) biological and medical image data, which would benefit tremendously from better frameworks for sharing (including technical, resourcing, legal, and ethical aspects).
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This presentaiton sumarises Euro-BioImaging ERIC services, focussing on their Image Data Services. It briefly presents processes and challenges in image anlaysis service provison and introduces some supporting tools. It also emphasises the roll of community initiatives and networks in providing solutions and support towards Image data management and analysis. This presentaiton was part of the GloBIAS BioImage Analysis Seminar Series.
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von Chamier, L., Laine, R.F., Jukkala, J., Spahn, C., Krentzel, D., Nehme, E., Lerche, M., Hernández-Pérez, S., Mattila, P.K., Karinou, E. and Holden, S., 2021. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nature communications, 12(1), pp.1-18.
Copy file name to clipboardExpand all lines: _sources/authors/anna_kreshuk.md
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This article outlines common challenges and practices when developing open-source software for bio-image analysis.
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Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we take a first step at presenting some of the most common use cases as well as critical/emerging use cases of (including the use of artificial intelligence for) biological and medical image data, which would benefit tremendously from better frameworks for sharing (including technical, resourcing, legal, and ethical aspects).
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The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023.
Tags: Bioimage Analysis, Python, Artificial Intelligence, Include In Dalia
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Content type: Video, Slides
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Content type: Slides, Code, Notebook
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Bioimage analysis (BIA), a crucial discipline in biological research, overcomes the limitations of subjective analysis in microscopy through the creation and application of quantitative and reproducible methods. The establishment of dedicated BIA support within academic institutions is vital to improving research quality and efficiency and can significantly advance scientific discovery. However, a lack of training resources, limited career paths and insufficient recognition of the contributions made by bioimage analysts prevent the full realization of this potential. This Perspective – the result of the recent The Company of Biologists Workshop ‘Effectively Communicating Bioimage Analysis’, which aimed to summarize the global BIA landscape, categorize obstacles and offer possible solutions – proposes strategies to bring about a cultural shift towards recognizing the value of BIA by standardizing tools, improving training and encouraging formal credit for contributions
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Copy file name to clipboardExpand all lines: _sources/authors/anne_e._carpenter.md
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This article outlines common challenges and practices when developing open-source software for bio-image analysis.
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Drosophila melanogaster Kc167 cells were stained for DNA (to label nuclei) and actin (a cytoskeletal protein, to show the cell body). Automatic cytometry requires that cells be segmented, i.e., that the pixels belonging to each cell be identified. Because segmenting nuclei and distinguishing foreground from background is comparatively easy for these images, the focus here is on finding the boundaries between adjacent cells.
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These images are of human HT29 colon cancer cells, a cell line that has been widely used for the study of many normal and neoplastic processes. A set of about 43,000 such images was used by Moffat et al. (Cell, 2006) to screen for mitotic regulators. The analysis followed the common pattern of identifying and counting cells with a phenotype of interest (in this case, cells that were in mitosis), then normalizing the count by dividing by the total number of cells. Such experiments present two image analysis problems. First, identifying the cells that have the phenotype of interest requires that the nuclei and cells be segmented. Second, normalizing requires an accurate cell count.
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This 384-well plate has images of co-cultured hepatocytes and fibroblasts. Every other well is populated (A01, A03, ..., C01, C03, ...) such that 96 wells comprise the data. Each well has 9 sites and thus 9 images associated, totaling 864 images.
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Since robust foreground/background separation and segmentation of cellular objects (i.e., identification of which pixels below to which objects) strongly depends on image quality, focus artifacts are detrimental to data quality. This image set provides examples of in- and out-of-focus HCS images which can be used for validation of focus metrics.
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Segmenting nuclei in 3D images can be challenging especially when nuclei are clustered not only in XY plane but also in XZ and YZ planes. Manually annotated ground truth provides a reference for image analysis software testing purposes. These images of mouse embryo blastocyst cells also have changing nuclei intensity in Z plane which makes finding the right threshold for successful segmentation a difficult task. This image set also contains GAPDH transcripts that can be quantified in each cell.
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This image set is part of a high-throughput chemical screen on U2OS cells, with examples of 200 bioactive compounds. The effect of the treatments was originally imaged using the Cell Painting assay (fluorescence microscopy). This data set only includes the DNA channel of a single field of view per compound. These images present a variety of nuclear phenotypes, representative of high-throughput chemical perturbations. The main use of this data set is the study of segmentation algorithms that can separate individual nucleus instances in an accurate way, regardless of their shape and cell density. The collection has around 23,000 single nuclei manually annotated to establish a ground truth collection for segmentation evaluation.
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Cell dynamics during the early mouse embryogenesis change spatiotemporally. For understanding the mechanism of this developmental process, imaging cell dynamics by live-cell imaging of fluorescently labeled nuclei and performing nuclei segmentation of these images by image processing are essential. This dataset contains the fluorescence images and Ground Truth used when performing nuclei segmentation using deep learning. Fluorescence images are time-series images from fertilization to blastocyst formation. Ground Truth is supervised data of the cell nuclear region.
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These are synthetic images from the Cell Tracking Challenge. The images depict simulated nuclei of HL60 cells stained with Hoescht (training datasets). These synthetic images of HL60 cells provide an opportunity to test image analysis software by comparing segmentation results to the available ground truth for each time point. The number of clustered nuclei increases with time adding more complexity to the problem. This time-laps dataset can be used for simple segmentation or for nuclei tracking.
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One of the principal challenges in counting or segmenting nuclei is dealing with clustered nuclei. To help assess algorithms performance in this regard, this synthetic image set consists of five subsets with increasing degree of clustering.
Copy file name to clipboardExpand all lines: _sources/authors/arrate_muñoz-barrutia.md
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von Chamier, L., Laine, R.F., Jukkala, J., Spahn, C., Krentzel, D., Nehme, E., Lerche, M., Hernández-Pérez, S., Mattila, P.K., Karinou, E. and Holden, S., 2021. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nature communications, 12(1), pp.1-18.
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Estibaliz Gómez-de-Mariscal, Hasini Jayatilaka, Özgün Çiçek, Thomas Brox, Denis Wirtz, Arrate Muñoz-Barrutia, *Search for temporal cell segmentation robustness in phase-contrast microscopy videos*, arXiv 2021 (arXiv:2112.08817)
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Bioimage analysis (BIA), a crucial discipline in biological research, overcomes the limitations of subjective analysis in microscopy through the creation and application of quantitative and reproducible methods. The establishment of dedicated BIA support within academic institutions is vital to improving research quality and efficiency and can significantly advance scientific discovery. However, a lack of training resources, limited career paths and insufficient recognition of the contributions made by bioimage analysts prevent the full realization of this potential. This Perspective – the result of the recent The Company of Biologists Workshop ‘Effectively Communicating Bioimage Analysis’, which aimed to summarize the global BIA landscape, categorize obstacles and offer possible solutions – proposes strategies to bring about a cultural shift towards recognizing the value of BIA by standardizing tools, improving training and encouraging formal credit for contributions
Copy file name to clipboardExpand all lines: _sources/authors/astrid_schauss.md
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Interdisciplinary collaboration and integration of large and diverse datasets are becoming increasingly important. Answering complex research questions requires combining and analysing multimodal datasets. Research data management follows the FAIR principles making data findable, accessible, interoperable, and reusable. However, there are challenges in capturing the entire research cycle and contextualizing data according, not only for the DataPLANT and NFDI4BIOIMAGE communities. To address these challenges, DataPLANT developed a data structure called Annotated Research Context (ARC). The Brain Imaging Data Structure (BIDS) originated from the neuroimaging community extended for microscopic image data. Both concepts provide standardised and file system based data storage structures for organising and sharing research data accompanied with metadata. We exemplarily compare the ARC and BIDS designs and propose structural and metadata mapping.
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Dieser hier enthaltene Beitrag ist der Initialvortrag des GerBi-Chats zum Teil 1 - Von der Bedarfsanmeldung bis zum Beginn der Antragststellung. Die weiteren Stufen der Großgerätebeschaffung werden in nachfolgenden Beiträgen behandelt.
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Nach dem Initialvortrag der GerBI-Chat Reihe, in dem das Thema Bedarfsanmeldung im Fokus stand, geht es im hier enthaltenen zweiten Teil „Antragsvorbereitung und -fertigstellung: Wie schreibe ich am besten einen Großgeräteantrag?“ um die Beantragung von Forschungsgroßgeräten nach Art. 91b GG.
Tags: Arc, Dataplant, Hackathon, Nfdi4Bioimage, OMERO, Python, Research Data Management
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Content type: Event, Publication, Documentation
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This repository documents the first NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne Hackathon), where topics like 'Interoperability', 'REMBI / Mapping', and 'Neuroglancer (OMERO / zarr)' were explored through collaborative discussions and workflow sessions, culminating in reports that bridge NFDI4Bioimage to DataPLANT. Funded by various DFG initiatives, this event emphasized documentation and use cases, contributing preparatory work for future interoperability projects at the 2nd de.NBI BioHackathon in Bielefeld.
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von Chamier, L., Laine, R.F., Jukkala, J., Spahn, C., Krentzel, D., Nehme, E., Lerche, M., Hernández-Pérez, S., Mattila, P.K., Karinou, E. and Holden, S., 2021. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nature communications, 12(1), pp.1-18.
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