Microscopy image analysis (14)#

BIDS-lecture-2024#

Robert Haase

Licensed CC-BY-4.0

Training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024.

Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python

Content type: Github Repository

ScaDS/BIDS-lecture-2024


Bio-image Data Science#

Robert Haase

Licensed CC-BY-4.0

This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python.

Tags: Image Data Management, Deep Learning, Microscopy Image Analysis, Python

Content type: Notebook

ScaDS/BIDS-lecture-2024


Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI#

Robert Haase

Licensed CC-BY-4.0

These are the PPTx training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024.

Tags: Bioimage Analysis, Deep Learning, Microscopy Image Analysis, Python

Content type: Slides

https://zenodo.org/records/12623730


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 Data, Microscopy Image Analysis

Content type: Forum Post

https://forum.image.sc/t/checklists-for-publishing-images-and-image-analysis/86304


Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep learning for microscopy#

Guillaume Jacquemet

Published 2024-10-24

Licensed UNKNOWN

Leukocyte extravasation is a critical component of the innate immune response, while circulating tumour cell extravasation is a crucial step in metastasis formation. Despite their importance, these extravasation mechanisms remain incompletely understood. In this talk, Guillaume Jacquemet presents a novel imaging framework that integrates microfluidics with high-speed, label-free imaging to study the arrest of pancreatic cancer cells (PDAC) on human endothelial layers under physiological flow conditions.

Tags: Deep Learning, Microscopy Image Analysis

Content type: Youtube Video, Slides

https://www.youtube.com/watch?v=KTdZBgSCYJQ


Example Pipeline Tutorial#

Tim Monko

Published 2024-10-28

Licensed BSD-3-CLAUSE

Napari-ndev is a collection of widgets intended to serve any person seeking to process microscopy images from start to finish. The goal of this example pipeline is to get the user familiar with working with napari-ndev for batch processing and reproducibility (view Image Utilities and Workflow Widget).

Tags: Napari, Microscopy Image Analysis, Bioimage Analysis

Content type: Documentation, Github Repository, Tutorial

https://timmonko.github.io/napari-ndev/tutorial/01_example_pipeline/

timmonko/napari-ndev


FAIRy deep-learning for bioImage analysis#

Estibaliz Gómez de Mariscal

Licensed CC-BY-4.0

Introduction to FAIR deep learning. Furthermore, tools to deploy trained DL models (deepImageJ), easily train and evaluate them (ZeroCostDL4Mic and DeepBacs) ensure reproducibility (DL4MicEverywhere), and share this technology in an open-source and reproducible manner (BioImage Model Zoo) are introduced.

Tags: Deep Learning, FAIR-Principles, Microscopy Image Analysis

Content type: Slides

https://f1000research.com/slides/13-147


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: Microscopy Image Analysis, Python, Deep Learning

Content type: Video, Slides

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


Multiplexed tissue imaging - tools and approaches#

Agustín Andrés Corbat, OmFrederic, Jonas Windhager, Kristína Lidayová

Licensed CC-BY-4.0

Material for the I2K 2024 “Multiplexed tissue imaging - tools and approaches” workshop

Tags: Bioimage Analysis, Microscopy Image Analysis

Content type: Github Repository, Slides, Workshop

BIIFSweden/I2K2024-MTIWorkshop

https://docs.google.com/presentation/d/1R9-4lXAmTYuyFZpTMDR85SjnLsPZhVZ8/edit#slide=id.p1


Open Micoscropy Environment (OME) Youtube Channel#

Published None

Licensed CC-BY-4.0

OME develops open-source software and data format standards for the storage and manipulation of biological microscopy data

Tags: Open Source Software, Microscopy Image Analysis, Bioimage Data

Content type: Video, Collection

https://www.youtube.com/@OpenMicroscopyEnvironment


The Open Microscopy Environment (OME) Data Model and XML file - open tools for informatics and quantitative analysis in biological imaging#

Ilya G. Goldberg, Chris Allan, Jean-Marie Burel, Doug Creager, Andrea Falconi, et. al

Published 2005-05-03

Licensed CC-BY-4.0

The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results.

Tags: Microscopy Image Analysis, Bioimage Analysis

Content type: Publication

https://genomebiology.biomedcentral.com/articles/10.1186/gb-2005-6-5-r47

https://doi.org/10.1186/gb-2005-6-5-r47


Towards community-driven metadata standards for light microscopy - tiered specifications extending the OME model#

Mathias Hammer, Maximiliaan Huisman, Alessandro Rigano, Ulrike Boehm, James J. Chambers, et al.

Published 2022-07-10

Licensed UNKNOWN

Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments.

Tags: Reproducibility, Microscopy Image Analysis, Metadata, Image Data Management, Bioimage Data

Content type: Publication

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271325/


Upcoming Image Analysis Events#

Curtis Rueden, Albane de la Villegeorges, Simon F. Nørrelykke, Romain Guiet, Olivier Burri, et al.

Licensed UNKNOWN

Tags: Bioimage Analysis, Microscopy Image Analysis

Content type: Collection, Event, Forum Post, Workshop

https://forum.image.sc/t/upcoming-image-analysis-events/60018/67


YMIA - Python-Based Event Series Training Material#

Riccardo Massei, Robert Haase, ENicolay

Published None

Licensed MIT

This repository offer access to teaching material and useful resources for the YMIA - Python-Based Event Series.

Tags: Python, Large Language Models, Prompt Engineering, Biabob, Bioimage Analysis, Microscopy Image Analysis

Content type: Github Repository, Slides

rmassei/ymia_python_event_series_material