Intermediate Image Analysis
A course for microscopists
Registration:
Please register as a PPMS order to book a place:
Author
Jeremy Pike, Image Analyst for COMPARE at the Univeristy of Birmingham
Summary
This course will cover common image analysis problems including deconvolution, denoising, segmentation, tracking and colocalization analysis. The course will focus on practical sessions using leading open source software.
Target Audience
- This course is recommended for all staff and students who regularly use microscopy in their research and would like to develop their analysis skills.
- Participants should have attended our introductory course, or have equivalent knowledge.
- Graduate students, postdocs and staff members from the Universities of Birmingham and Nottingham, affiliated institutions and other external institutions or individuals. In the first instance priority should be given to COMPARE researchers.
Aims and Objectives
After this course particpants should be able to:
- Perform deconvolution of wide-field datasets.
- Use self-supervised deep learning based denosing
- Use advanced segmentation algorithms, to include machine learning and model based approaches.
- Track objects in time-lapse data, for example cell tracking.
- Understand and implement good practice for colocalization analysis. We will focus on pixel based approaches as object based analysis will be covered in our SMLM course.
- Automate analysis workflows using ImageJ macro language.
Course Materials
Note, this material was adapted from a course run at the Cancer Research UK Cambridge institute.
- Lecture Slides
- Exercises
- Sample Data Zip file
- Windows ilastik installation (no admin rights needed)
- StarDist macro
- Colocalization macro (single file)
- Colocalization macro (batch)
Course Software
In this course participants will use a range of open source software and plugins: