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Recommended Readings and Viewings

  • This is a curated, non exhaustive, list of useful links pointing at training courses, knowledge bases, books, and tutorials. The topics or software addressed here are intended to cover what is currently available at the platform.
  • The CIF has no relationship with any of the links presented here, and this list is only meant to help the researcher in his daily work.
  • The links are presented in no particular order, but they are sorted by category.

If you see any broken link, please tell us immediately using the contact form on the bottom of the home page to keep this page as relevant as possible. You can also submit new links or ideas the same way.

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Must-read papers

These are essential papers to any scientist working with biological images. Make sure to read them and put them in your library.

TopicDescriptionLinkType
Roeder, Adrienne H. K., Alexandre Cunha, Michael C. Burl, et Elliot M. Meyerowitz. « A Computational Image Analysis Glossary for Biologists ». Development 139, no 17 (1 septembre 2012): 3071‑80. 

Summary

Recent advances in biological imaging have resulted in an explosion in the quality and quantity of images obtained in a digital format. Developmental biologists are increasingly acquiring beautiful and complex images, thus creating vast image datasets. In the past, patterns in image data have been detected by the human eye. Larger datasets, however, necessitate high-throughput objective analysis tools to computationally extract quantitative information from the images. These tools have been developed in collaborations between biologists, computer scientists, mathematicians and physicists. In this Primer we present a glossary of image analysis terms to aid biologists and briefly discuss the importance of robust image analysis in developmental studies.

Read the paper

Jen-Yi Lee and Maiko Kitaoka, “A Beginner’s Guide to Rigor and Reproducibility in Fluorescence Imaging Experiments,” Molecular Biology of the Cell 29, no. 13 (01 2018): 1519–25, https://doi.org/10.1091/mbc.E17-05-0276.

 

Abstract

Fluorescence light microscopy is an indispensable approach for the investigation of cell biological mechanisms. […]

This Perspective provides a basic “best practices” guide for designing and executing fluorescence imaging experiments, with the goal of introducing researchers to concepts that will help empower them to acquire images with rigor.

 

Read the paper
Waters, Jennifer C. « Accuracy and precision in quantitative fluorescence microscopy ». The Journal of Cell Biology 185, no 7 (29 juin 2009): 1135‑48.

Abstract

The light microscope has long been used to document the localization of fluorescent molecules in cell biology research. With advances in digital cameras and the discovery and development of genetically encoded fluorophores, there has been a huge increase in the use of fluorescence microscopy to quantify spatial and temporal measurements of fluorescent molecules in biological specimens. Whether simply comparing the relative intensities of two fluorescent specimens, or using advanced techniques like Förster resonance energy transfer (FRET) or fluorescence recovery after photobleaching (FRAP), quantitation of fluorescence requires a thorough understanding of the limitations of and proper use of the different components of the imaging system. Here, I focus on the parameters of digital image acquisition that affect the accuracy and precision of quantitative fluorescence microscopy measurements.

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James Jonkman et al., “Tutorial: Guidance for Quantitative Confocal Microscopy,” Nature Protocols, March 31, 2020, 1–27, https://doi.org/10.1038/s41596-020-0313-9.

Abstract

[…] In this tutorial, the researcher is guided through all aspects of acquiring quantitative confocal microscopy images, including optimizing sample preparation for fixed and live cells, choosing the most suitable microscope for a given application and configuring the microscope parameters. Suggestions are offered for planning unbiased and rigorous confocal microscope experiments. Common pitfalls such as photobleaching and cross-talk are addressed, as well as several troubling instrumentation problems that may prevent the acquisition of quantitative data. Finally, guidelines for analyzing and presenting confocal images in a way that maintains the quantitative nature of the data are presented, and statistical analysis is discussed.[…]

Read the paper
Statistics for Biologists (Nature collection)

This collection highlights important statistical issues that biologists should be aware of and provides practical advice to help them improve the rigor of their work.

Go to the Collection

Data Management

How to organize your data

This is probably the most important thing you have to master before focusing on any other imaging problems.

TopicDescriptionLinkType

UNIL Open Science Data

Data management plan, file naming, folder structure

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Greg Wilson et al., “Best Practices for Scientific Computing,” PLOS Biology 12, no. 1 (January 7, 2014): e1001745, https://doi.org/10.1371/journal.pbio.1001745.

[…]This paper describes a set of practices that are easy to adopt and have proven effective in many research settings. Our recommendations are based on several decades of collective experience both building scientific software and teaching computing to scientists [17],[18], reports from many other groups [19]–, guidelines for commercial and open source software development [26],, and on empirical studies of scientific computing [28][31] and software development in general (summarized in [32]). […]Read the paper

Greg Wilson et al., “Good Enough Practices in Scientific Computing,PLOS Computational Biology 13, no. 6 (June 22, 2017): e1005510, https://doi.org/10.1371/journal.pcbi.1005510

[…]This paper presents a set of good computing practices that every researcher can adopt, regardless of their current level of computational skill. […]Read the paper

Image Processing

Must-read papers

These are essential papers to any scientist working with biological images. Make sure to read them and put them in your library.

TopicDescriptionLinkType
Cromey, Douglas W. « Digital Images Are Data: And Should Be Treated as Such ». Methods in molecular biology (Clifton, N.J.) 931 (2013): 1‑27.

Abstract

The scientific community has become very concerned about inappropriate image manipulation. In journals that check figures after acceptance, 20–25% of the papers contained at least one figure that did not comply with the journal’s instructions to authors. The scientific press continues to report a small, but steady stream of cases of fraudulent image manipulation. Inappropriate image manipulation taints the scientific record, damages trust within science, and degrades science’s reputation with the general public. Scientists can learn from historians and photojournalists, who have provided a number of examples of attempts to alter or misrepresent the historical record. Scientists must remember that digital images are numerically sampled data that represent the state of a specific sample when examined with a specific instrument. These data should be carefully managed. Changes made to the original data need to be tracked like the protocols used for other experimental procedures. To avoid pitfalls, unexpected artifacts, and unintentional misrepresentation of the image data, a number of image processing guidelines are offered.

Keywords: Digital image, Ethics, Manipulation, Image processing, Microscopy
Read the paper

ImageJ | FIJI

Image J is the go-to open-source image processing software for many scientists. The FIJI distribution is a complete package dedicated to the biologist and microscopist

TopicDescriptionLinkType

ImageJ Techniques

General ImageJ concepts, skills, and techniques

Go to the website
FIJI Scripting TutorialLearn how to program FIJI scripts in PythonGo to the website
ImageJ user guidesE-Books and recommended booksSee the book list
FIJI for Quantification
Video tutorial by the Melbourne Advanced Microscopy FacilityYoutube playlist
FIJI for Beginners
Video playlist by the Melbourne Advanced Microscopy FacilityYoutube playlist

Imaris

Imaris is a commercial package focusing on 3D data sets, whether they are stacks, multichannel, time series, or any other data requiring 3D visualization and processing.

TopicDescriptionLinkType

Bitplane learning center

Collection of video tutorials, webinars recordings and white papers from Bitplane

Go to the website

QuPath

Image processing software package that focuses on data sets coming from slide scanners, but can be applied to other types of images as well.

TopicDescriptionLinkType
Download QuPath

Main website

Get QuPath here

QuPath official documentation

“Read The Docs” type documentation

Read The Docs

Getting started with QuPath

Video walkthrough by QuPath’s author, Pete Bankhead

Youtube playlist

QuPath case study : IHC analysis

Advanced topics with QuPath

Youtube playlist

Advanced QuPath : “From samples to knowledge 2020” (2-days workshop video recording)

QuPath 2020 workshop “From Samples to Knowledge”, which took place at La Jolla Insitute for Immunology on February 20-21, 2020. www.lji.org @ljiresearch @LJIMicroCore

Youtube playlist

CellProfiler

Image processing software package that focuses on large data sets composed of possibly millions of files. Idealy suited for high throughput content, but it can also be used on single images as well. The learning curve is quite steep.

TopicDescriptionLinkType

CellProfiler Overview

Video walkthrough by Gopal Karemore (workshop recording)

Youtube video

Broad Institute tutorials

Various use cases by the group behind Cell Profiler at the Broad Institute. From beginners to cell classification via machine learning.

Youtube playlist

Python

Python is a popular programming language, easy to learn. It comes with the flexibility of many optional modules developed by the community.

TopicDescriptionLinkType

BIAPy: BioImage Analysis with Python, by Guillaume Witz

This course is an introduction to bioimage processing with Python and Jupyter. It is composed of a series of Jupyter notebooks that can be simply read or run interactively using either the binder service, the Google Colab service or via a local installation. The course covers basics, such as handling images as Numpy arrays, as well as a few more advanced topics such as tracking, registration etc. While some content presents fundamental concepts in image processing, this is NOT an image processing course. The goal of this cours is to give people with image processing background (e.g. in Fiji) the opportunity to discover how image processing is done in the Python world. In particular this course is not exhaustive as it only covers selected topics.

GitHub Repository
Python for Microscopy, by Sreeni

Video tutorials by Dr Sreenivas : learn Python and modules useful in microscopy, case studies, fundamental concepts illustrated using Python.

Youtube playlists

Image Processing Guides and Books

A collection of books and guides on image processing in general. Some of them make extensive use of one specific package (Fiji, MATLAB, …), but others a more generic.

TopicDescriptionLinkType
Analyzing Fluorescence Microscopy Images with ImageJ (by Pete Bankhead)

This work is made available in the hope it will be useful to researchers in biology who need to quickly get to grips with the main principles of image analysis.

Read the book
BioImage Data Analysis (by Kota Myura)

This book is the result of combining the teaching materials from a select group of Image Data Analysis experts. It focusses on the needs of the person doing the analysis, finding the perfect balance between IT and Biology knowledge needed to do good science.

Get the book

Kota Miura and Natasa Sladoje, eds., Bioimage Data Analysis Workflows, Learning Materials in Biosciences (Springer International Publishing, 2020), https://doi.org/10.1007/978-3-030-22386-1.

This Open Access textbook provides students and researchers in the life sciences with essential practical information on how to quantitatively analyze data images. It refrains from focusing on theory, and instead uses practical examples and step-by step protocols to familiarize readers with the most commonly used image processing and analysis platforms such as ImageJ, MatLab and Python. Besides gaining knowhow on algorithm usage, readers will learn how to create an analysis pipeline by scripting language; these skills are important in order to document reproducible image analysis workflows.

The textbook is chiefly intended for advanced undergraduates in the life sciences and biomedicine without a theoretical background in data analysis, as well as for postdocs, staff scientists and faculty members who need to perform regular quantitative analyses of microscopy images.

Get the book

General Imaging & Microscopy

Fluorescence staining

Most of modern optical optical microscopy is based on fluorescent probes. You will find here links related to their use in microscopy.

TopicDescriptionLinkType

Handbook of Fluorescent Probes

The complete Molecular Probes book on fluorescent dyes. Probes for each and every use case.

Get the book

Primers and Knowledge Bases

Knowledge bases are covering all the key microscopy concepts. They are often written by microscopy companies to help their customers, but they also address more general topics, independently from their products. You will also find extensive documentation on brand-specific technologies like Zeiss Airyscan or Leica STED.

TopicDescriptionLinkType

Olympus Microscopy Resource Center

Covers topics ranging from the physics of light to virtual microscopy.Go to the Primer

Nikon Microscopy U

Website dedicated to microscopy education. Many articles on key concepts of all imaging domains, from physics, to detectors and cameras, and even ideal posture in front of a microscopeGo to the Primer
Zeiss CampusSame principle as other similar knowledge base, with interactive tutorials and techniques review articlesGo to the Primer
Leica Science Lab
Also similar to the other education sites by microscopy brands, there a re interactive tutorials, webinars recordings, as well as a community aspect to it.Go to the Primer