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4 results for “Ilastik”
Ilastik_organoid_segmentation
<p>This repository holds two Ilastik projects and respective training data used for the organoid segmentation of the initial organoid screen in the publication "A semi-automated intestinal organoid screening method demonstrates epigenetic control of epithelial differentiation" (<a href="https://doi.org/10.3389/fcell.2020.618552">Ostrop et al. 2020</a>).</p> <p>The image pre-processing was carried out with the ImageJ script available on GitHub: <a href="https://github.com/jennyostrop/Fiji_organoid_brightfield_processing">https://github.com/jennyostrop/Fiji_organoid_brightfield_processing</a>, archived on Zenodo as <a href="https://doi.org/10.5281/zenodo.3951125">https://doi.org/10.5281/zenodo.3951125</a></p> <p>For training, 1 well treated with DMSO vehicle control of 4 biological replicates and 5 timepoints were used (total 20 images). Training data was excluded from further analysis.</p> <p>The raw images were deposited to the Image Data Resource (<a href="https://idr.openmicroscopy.org">https://idr.openmicroscopy.org</a>) under accession number idr0092.<br> Training data corresponds to Well A01 in plate 1-plate 4.</p> <p> </p> <p><strong>Project 1: Ilastik1_PixelClass_Edges</strong></p> <p>Pixel classification, created with Ilastik 1.3.2</p> <p>Input Raw Data: Summary projection of Sobel edge detection for each z-layer (Output 2d from ImageJ script)</p> <p>Training classes:<br> Background: lines over background and debris (size 7) and inside of organoids (size 3)<br> Object: exact lines following the outer border of organoids (size 1)</p> <p> </p> <p><strong>Project 2: Ilastik2_ObjectClass_Projection</strong></p> <p>Object classification [Inputs: Raw Data, Pixel Prediction Map], created with Ilastik 1.3.2</p> <p>Input Raw Data: Minimum intensity projections of each Zstack (Output 1b from ImageJ script)<br> Input Prediction Maps: Pixel Prediction Maps generated in project Ilastik1_PixelClass_Edges</p> <p>Threshold and Size Filter: Method - Simple, Input - 0 (Background), Smooth – 1.0, 1.0, Threshold - 0.55, Size Filter – Min 10, Max 1000000<br> <br> Object classes:<br> Mislabelled: Patch of background enclosed by objects, light<br> Organoid: Typical organoid, medium dark<br> Sphere_big: Sphere, almost round, light, size of organoids or bigger<br> Sphere_small: Sphere, almost round, light to medium dark, small<br> Cluster: Cluster of several organoids/spheres and organoids segmented as single object<br> Debris: Debris in Matrigel, irregular borders and structure, mostly small, medium dark<br> AirBubble: Air bubbles in Matrigel at early timepoints, round, dark with light centre<br> Edges: Edges of well plate (empty class, included for consistency)</p>
Microstructure images, ilastik labels and probability maps.
<p>Microstructure images of several Al alloys reinforced with quasicrystals. Images were obtained using a Zeiss Axio Imager.A1m with AxioCam ERc 5s. Original images were enhanced using FIJI and analysed using ilastik. Included is the ilastik training set, labels used and probability maps.</p>
Ilastik project for Platynereis chromatin segmentation
<p>Ilastik project & associated images used for chromatin segmentation in the publication Whole-body integration of gene expression and single-cell morphology. For details on how this project was used see: https://github.com/platybrowser/platybrowser-backend/tree/master/mmpb/segmentation/chromatin</p> <p>Ilastik - <a href="https://www.ilastik.org/">https://www.ilastik.org/</a> - Berg, S., Kutra, D., Kroeger, T. <em>et al.</em> ilastik: interactive machine learning for (bio)image analysis. <em>Nat Methods</em> <strong>16, </strong>1226–1232 (2019). https://doi.org/10.1038/s41592-019-0582-9</p>
Ilastik project for segmenting UPEC
<p>Ilastik project for segmenting UPEC</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.