Image Analysis course with Fiji/ImageJ ONLINE
Monday, 23 October 2023 to Friday, 27 October 2023, 9.30am - 12pmApply for this course
This four half day online interactive sessions are suitable for all MSD postgraduate research students and postdoctoral early career researchers involved with bright field and fluorescence microscopy.
You are expected to attend four sessions:
- Monday 23 October, 09:30 - 12:00
- Tuesday 24 October, 09:30 - 12:00
- Wednesday 25 October, 09:30 - 12:00
- Friday 27 October, 09:30 - 12:00
This course consists of four 2.5 hour live interactive online sessions that aims to provide the basic skills necessary to tackle the most common image analysis tasks. Attendees will be shown a variety of techniques and worked examples. They will also learn where and how to access additional help in the future.
At the tutor’s discretion, some of the scheduled sessions may run into the afternoon if extra time is needed.
This course will cover the following topics:
- How to interact with the Fiji/ImageJ interface and functions
- The basic approaches to segmentation and intensity measurements in 2D and 3D
- How to manage measurements through curating regions of interest
- How to use image filtering
- How to perform colocalisation analysis
- Recommended plugins and image analysis tools
- How to keep Fiji/ImageJ up-to-date and how to install plugins.
- How programming and scripting works in Fiji/ImageJ
- How to use machine learning for image segmentation and de-noising
- Best practise with regards to microscopy image analysis and how to avoid common pitfalls
- Where to find online resources for continued development
At the end of the course, participants will be able to
- Interact with the Fiji/ImageJ interface and functions
- Understand the basic principles of Image Analysis and apply them to their images
- Use machine learning for image segmentation and de-noising
- Undertake programming and scripting in Fiji/ImageJ
- Keep Fiji/ImageJ up-to-date and install plugins.
HOW IT WILL WORK
Booking confirmation and reminder emails will send you the link for the live interactive sessions (held in Teams) and joining instructions. To join the session, you can click on the link 15 minutes before the session. Once the tutor has started the session they will tell you what to do. The tutor will share his screen with the slides. At the end of the lecture there will be exercises - the tutor will then go through the exercises and the solutions. Questions and responses will also be managed in the chat.
WHAT YOU WILL NEED
A good internet connection, uninterrupted time, camera and microphone enabled on your PC.
Having a computer with two monitors may be beneficial. Before the course, you will need to download Fiji. An email with the instructions will be sent to you.
ATTENDANCE CERTIFICATE ON SURVEY COMPLETION
It is now a requirement that you complete the three short questions in the survey you receive after attending the course. Once you have submitted the survey, you will be sent an email with a link to your attendance certificate. This is to ensure we receive the feedback we need to evaluate and improve our courses. Survey results are downloaded and stored anonymously.
Where no cost is indicated in the shopping trolley, no deposit is required. However, more than two non-attendances or late cancellations without good reason will be logged and may mean you cannot attend any further MSD training that term. Please refer to our Terms and Conditions for further information.
FEEDBACK FROM PREVIOUS ONLINE SESSION
"Excellent instructor and course content. Really enjoyed Lior's teaching and supportive manner. I learnt a lot of very applicable tools that I will apply to my research. I will be recommending it to my colleagues."
"This workshop is indeed the best so far I have attended not only it’s useful but it’s also well organized and presented. Moreover, we have time for practising! This helps a lot. Thanks a lot!"
"I thought the course was very well explained and I found everything easy to understand. I hadn't had any previous experience with image analysis before this course so thank you Lior."