Image Analysis for Biologists

PrerequisitesUpdated

This course will focus on computational methods for analysing cellular images and extracting quantitative data from them. The aim of this course is to familiarise the participants with computational image analysis methodologies, and to provide hands-on training in running quantitative analysis pipelines.

On day 1 we will introduce principles of image processing and analysis, giving an overview of commonly used algorithms through a series of talks and practicals based on Fiji, an extensible open source software package.

On day 2, we will focus on machine learning and computer vision for the analysis of images in cell biology. We will introduce the methodology in a series of lectures and show their application in the hands-on session. These practical sessions will be based on CellCognition, a tool for the analysis of live cell imaging data.

On day 3, we will describe the open Icy platform developed at the Institut Pasteur. Icy is a next-generation, user-friendly software offering powerful acquisition, visualization, annotation and analysis algorithms for 5D bioimaging data, together with unique automation/scripting capabilities (notably via its graphical programming interface) and tight integration with existing software (e.g. ImageJ, Matlab, Micro-Manager).

The timetable can be found here.

This event is sponsored by the Systems Microscopy NoE.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to Book or register Interest by linking here.

• Researchers who are applying or planning to apply image analysis in their research
• Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals
• Further details regarding eligibility criteria are available here
• Further details regarding the charging policy are available here

Basic skills in mathematics and programming are an advantage, but not a requirement.

Number of sessions: 3

After this course you should be able to:

• discuss the basic principles of machine learning for cellular phenotyping
• apply machine learning based workflows to image data
• apply workflows for the analysis of High Content Screening data

During this course you will learn about:

• commonly used algorithms for image analysis
• handling, processing and analyzing images in Fiji
• introduction to machine learning for bioimage analysis
• cellular phenotyping and analysis of High Content Screening data
• focus on live cell imaging data

Presentations, demonstrations and practicals

3

Once a year