Www.ebi.ac.uk (18)

Www.ebi.ac.uk (18)#

3D Ground Truth Annotations of Nuclei in 3D Microscopy Volumes#

Alain Chen, Liming Wu, Seth Winfree, Kenneth Dunn, Paul Salama, Edward Delp, Teresa Zulueta-Coarasa

Published 2024-12-20

Licensed CC-BY-4.0

This submission contains a set of 3D microscopy volumes of cell nuclei from different species and tissues that have been manually segmented. We also provide synthetically generated 3D microscopy volumes that can be used for training segmentation methods.

Tags: Ai-Ready

Content type: Data

https://www.ebi.ac.uk/bioimage-archive/galleries/ai/analysed-dataset/S-BIAD1518/

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

3D light-sheet microscopy data for SELMA3D 2024 challenge - Training subset with annotations#

Ying Chen, Johannes C. Paetzold, Ali Erturk, Doris Kaltenecker, Mihail Todorov, Harsharan Singh Bhatia, Shan Zhao, Luciano Höher

Published 2024-06-05

Licensed CC-BY-4.0

This dataset is the training set with annotations for the SELMA3D challenge. The SELMA3D challenge focuses on self-supervised learning for 3D light-sheet microscopy image segmentation. Its objective is to encourage the development of self-supervised learning methods for general segmentation of various structures in 3D light-sheet microscopy images. The dataset comtains 3D image patches of different labeled biological structures in the brain, including blood vessels, c-Fos labeled brain cells involved in neural activity, cell nuclei, and Alzheimers disease plaques. Each patch includes corresponding pixel-wise annotations for the labeled structures.

Tags: Ai-Ready

Content type: Data

https://www.ebi.ac.uk/bioimage-archive/galleries/ai/analysed-dataset/S-BIAD1196/

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

An image-based data-driven analysis of cellular architecture in a developing tissue#

Jonas Hartmann, Mie Wong, Elisa Gallo, Darren Gilmour

Published 2022-12-13

Licensed CC-BY-4.0

3D zebrafish embryo images with single-cell segmentation and point cloud-based morphometry

Tags: Ai-Ready

Content type: Data

https://www.ebi.ac.uk/bioimage-archive/galleries/S-BIAD599-ai.html

Bioimage Archive#

Content type: Collection, Data, Publication

https://www.ebi.ac.uk/bioimage-archive/

https://www.sciencedirect.com/science/article/abs/pii/S0022283622000791

EMBL-EBI material collection#

EMBL-EBI

Licensed CC0 (MOSTLY, BUT CAN DIFFER DEPENDING ON RESOURCE)

Online tutorial and webinar library, designed and delivered by EMBL-EBI experts

Tags: Bioinformatics

Content type: Collection

https://www.ebi.ac.uk/training/on-demand?facets=type:Course%20materials&query=

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

Finding and using publicly available data#

Anna Swan

Published 2024-01-01

Licensed CC-BY-4.0

Sharing knowledge and data in the life sciences allows us to learn from each other and built on what others have discovered. This collection of online courses brings together a variety of training, covering topics such as biocuration, open data, restricted access data and finding publicly available data, to help you discover and make the most of publicly available data in the life sciences.

Tags: Open Science, Teaching, Sharing

Content type: Collection, Tutorial, Video

https://www.ebi.ac.uk/training/online/courses/finding-using-public-data/

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/

Methods in bioimage analysis#

Christian Tischer

Licensed CC-BY-4.0

Tags: Bioimage Analysis

Content type: Online Tutorial, Video, Slides

https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/

https://doi.org/10.6019/TOL.BioImageAnalysis22-w.2022.00001.1

https://drive.google.com/file/d/1MhuqfKhZcYu3bchWMqogIybKjamU5Msg/view

Microscopy data analysis: machine learning and the BioImage Archive#

Andrii Iudin, Anna Foix-Romero, Anna Kreshuk, Awais Athar, Beth Cimini, Dominik Kutra, Estibalis Gomez de Mariscal, Frances Wong, Guillaume Jacquemet, Kedar Narayan, Martin Weigert, Nodar Gogoberidze, Osman Salih, Petr Walczysko, Ryan Conrad, Simone Weyend, Sriram Sundar Somasundharam, Suganya Sivagurunathan, Ugis Sarkans

Licensed CC-BY-4.0

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

Content type: Video, Slides

https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/

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

https://www.ebi.ac.uk/bioimage-archive/rembi-help-overview/

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

https://www.ebi.ac.uk/bioimage-archive/submit/

Synthetic images and segmentation masks simulating HL-60 cell nucleus in 3D#

David Svoboda, Michal Kozubek, Stanislav Stejskal, Teresa Zulueta-Coarasa

Published 2024-11-26

Licensed CC-BY-3.0

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 four subsets with increasing degree of clustering. Each subset is also provided in two different levels of quality: high SNR and low SNR.

Tags: Ai-Ready

Content type: Data

https://www.ebi.ac.uk/bioimage-archive/galleries/ai/analysed-dataset/S-BIAD1492/

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