Cc0-1.0 (25)#
3D cell shape of Drosophila Wing Disc#
Giulia Paci, Ines Fernandez Mosquera, Pablo Vicente Munuera, Yanlan Mao
Published 2023-08-14
Licensed CC0-1.0
Segmentation masks of individual cells in Drosophila wing discs
Tags: Ai-Ready
Content type: Data
https://www.ebi.ac.uk/bioimage-archive/galleries/S-BIAD843-ai.html
A Fiji Scripting Tutorial#
Albert Cardona
Licensed CC0-1.0
Tags: Imagej, Bioimage Analysis
Content type: Notebook
An annotated fluorescence image dataset for training nuclear segmentation methods#
Sabine Taschner-Mandl, Inge M. Ambros, Peter F. Ambros, Klaus Beiske, Allan Hanbury, Wolfgang Doerr, Tamara Weiss, Maria Berneder, Magdalena Ambros, Eva Bozsaky, Florian Kromp, Teresa Zulueta-Coarasa
Published 2023-03-07
Licensed CC0-1.0
Ground-truth annotated fluorescence image dataset for training nuclear segmentation methods
Tags: Ai-Ready
Content type: Data
https://www.ebi.ac.uk/bioimage-archive/galleries/S-BIAD634-ai.html
Andor Dragonfly confocal image of BPAE cells stained for actin, IMS file format#
Hoku West-Foyle
Published 2025-01-16
Licensed CC0-1.0
BioImage Archive AI Gallery#
Licensed CC0-1.0
Tags: Bioimage Analysis, Artificial Intelligence
Content type: Collection, Data
BioImage Archive Visual Gallery#
Licensed CC0-1.0
Tags: Bioimage Analysis
Content type: Collection, Data
https://www.ebi.ac.uk/bioimage-archive/galleries/visualisation.html
BioImage Archive Volume EM Gallery#
Licensed CC0-1.0
Tags: Bioimage Analysis
Content type: Collection, Data
Breast Cancer Semantic Segmentation (BCSS) dataset#
Mohamed Amgad, Habiba Elfandy, Hagar Hussein, Lamees A Atteya, Mai A T Elsebaie, Lamia S Abo Elnasr, Rokia A Sakr, Hazem S E Salem, Ahmed F Ismail, Anas M Saad, Joumana Ahmed, Maha A T Elsebaie, Mustafijur Rahman, Inas A Ruhban, Nada M Elgazar, Yahya Alagha, Mohamed H Osman, Ahmed M Alhusseiny, Mariam M Khalaf, Abo-Alela F Younes, Ali Abdulkarim, Duaa M Younes, Ahmed M Gadallah, Ahmad M Elkashash, Salma Y Fala, Basma M Zaki, Jonathan Beezley, Deepak R Chittajallu, David Manthey, David A Gutman, Lee A D Cooper
Published 2019-11-09
Licensed CC0-1.0
This repo contains the necessary information and download instructions to download the dataset associated with the paper: Amgad M, Elfandy H, …, Gutman DA, Cooper LAD. Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics. 2019. doi: 10.1093/bioinformatics/btz083. This data can be visualized in a public instance of the Digital Slide Archive at this link. If you click the “eye” image icon in the Annotations panel on the right side of the screen, you will see the results of a collaborative annotation.
Tags: Ai-Ready
Content type: Data
Checklists for publishing images and image analysis#
Christopher Schmied
Published 2023-09-14
Licensed CC0-1.0
In this paper we introduce two sets of checklists. One is concerned with the publication of images. The other one gives instructions for the publication of image analysis.
Tags: Bioimage Analysis
Content type: Forum Post
https://forum.image.sc/t/checklists-for-publishing-images-and-image-analysis/86304
Drosophila Kc167 cells#
Vebjorn Ljosa, Katherine L. Sokolnicki, Anne E. Carpenter
Published 2012-06-28
Licensed CC0-1.0
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.
Tags: Ai-Ready
Content type: Data
Embryonic mice ultrasound volumes with body and brain volume segmentation masks#
Ziming Qiu, Matthew Hartley
Published 2023-05-10
Licensed CC0-1.0
Ultrasound images of mouse embryos with body and brain volume segmentation masks
Tags: Ai-Ready
Content type: Data
https://www.ebi.ac.uk/bioimage-archive/galleries/S-BIAD686-ai.html
Galaxy workflows#
Licensed CC0-1.0
A workflow is a chain of analysis steps. In Galaxy, we can create a workflow from an existing analysis history, or we can create one visually by adding tools to a canvas. This tutorial covers building a workflow to analyse a bacterial genome, from input FASTQ sequencing reads to assembly, annotation, and visualization.
Tags: Workflow
Content type: Online Tutorial, Tutorial
https://galaxy-au-training.github.io/tutorials/modules/workflows/
Go-Nuclear. A deep learning-based toolkit for 3D nuclei segmentation and quantitative analysis in cellular and tissue context#
Kay Schneitz, Athul Vijayan, Tejasvinee Mody
Published 2024-06-29
Licensed CC0-1.0
We present computational tools that allow versatile and accurate 3D nuclear segmentation in plant organs, enable the analysis of cell-nucleus geometric relationships, and improve the accuracy of 3D cell segmentation. This biostudies submission includes Arabidopsis ovule model training dataset used in the study. The training dataset is composed of strong and weak nuclei image channels, corresponding ground truth segmentation, cell wall image and associated cell segmentation mentioned in the study. Trained models from the study, a total of 47 trained models are made available from this study. This included 15 initial models, 30 gold models, and 2 platinum models. Models were trained using PlantSeg, Stardist and Cellpose. All image datasets and its segmentation as part of the figures in this study is also available as separate zip files. This includes image dataset from different species and organs as listed below.
Tags: Ai-Ready
Content type: Data
https://www.ebi.ac.uk/bioimage-archive/galleries/ai/analysed-dataset/S-BIAD1026/
Human U2OS cells (out of focus)#
Vebjorn Ljosa, Katherine L. Sokolnicki, Anne E. Carpenter
Published 2012-06-28
Licensed CC0-1.0
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.
Tags: Ai-Ready
Content type: Data
Image-based Profiling Handbook#
Beth Cimini, Tim Becker, Shantanu Singh, Gregory Way, Hamdah Abbasi, Callum Tromans-Coia
Licensed CC0-1.0
Tags: Bioimage Analysis
Content type: Book
Ink in a dish#
Cavanagh
Published 2024-09-03
Licensed CC0-1.0
A test data set for troublshooting. no scientific meaning.
Mouse embryo blastocyst cells#
Vebjorn Ljosa, Katherine L. Sokolnicki, Anne E. Carpenter
Published 2012-06-28
Licensed CC0-1.0
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.
Tags: Ai-Ready
Content type: Data
Nuclei of U2OS cells in a chemical screen#
Vebjorn Ljosa, Katherine L. Sokolnicki, Anne E. Carpenter
Published 2012-06-28
Licensed CC0-1.0
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.
Tags: Ai-Ready
Content type: Data
Nuclei of mouse embryonic cells#
Vebjorn Ljosa, Katherine L. Sokolnicki, Anne E. Carpenter
Published 2012-06-28
Licensed CC0-1.0
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.
Tags: Ai-Ready
Content type: Data
Online_R_learning#
C. Li
Published 2023-07-09T06:27:14+00:00
Licensed CC0-1.0
Online R learning for applied statistics
Tags: Statistics
Content type: Github Repository
REMBI Overview#
Licensed CC0-1.0
Recommended Metadata for Biological Images (REMBI) provides guidelines for metadata for biological images to enable the FAIR sharing of scientific data.
Tags: FAIR-Principles, Metadata, Research Data Management
Content type: Collection
Simulated HL60 cells (from the Cell Tracking Challenge)#
Vebjorn Ljosa, Katherine L. Sokolnicki, Anne E. Carpenter
Published 2012-06-28
Licensed CC0-1.0
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.
Tags: Ai-Ready
Content type: Data
Statistical Rethinking#
Richard McElreath
Published 2024-03-01
Licensed CC0-1.0
This course teaches data analysis, but it focuses on scientific models. The unfortunate truth about data is that nothing much can be done with it, until we say what caused it. We will prioritize conceptual, causal models and precise questions about those models. We will use Bayesian data analysis to connect scientific models to evidence. And we will learn powerful computational tools for coping with high-dimension, imperfect data of the kind that biologists and social scientists face.
Tags: Statistics
Content type: Github Repository
Submitting data to the BioImage Archive#
Licensed CC0-1.0
To submit, you’ll need to register an account, organise and upload your data, prepare a file list, and then submit using our web submission form. These steps are explained here.
Tags: Research Data Management
Content type: Tutorial, Video
Synthetic cells#
Vebjorn Ljosa, Katherine L. Sokolnicki, Anne E. Carpenter
Published 2012-06-28
Licensed CC0-1.0
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 synthetic images, which can be used for validation of focus metrics.
Tags: Ai-Ready
Content type: Data
https://bbbc.broadinstitute.org/BBBC005