Image Analysis course with Fiji/ImageJ

We would like to invite you to an online course introducing you to the basics of image analysis, including automatic segmentation, colocalisation, denoising, 3D, etc.

We scheduled two new sessions:

May 2024 session: more details and sign up here.
(Mon, 6 May to Fri, 10 May 2024)

June 2024 session: more details and sign up here.
(Mon, 17 Jun to Fri, 21 Jun 2024)

What You’ll Learn

This course consists of five 2.5-hour live interactive online sessions, where you’ll dive into a diverse range of image analysis techniques and practical examples. Here’s a glimpse of what we will cover during the course:

• Automatic segmentation and object counting in 2D and 3D
• Colocalization analysis for studying interactions within your samples
• Leveraging deep learning for denoising and segmentation
• Automation of tasks using macros
• And much more

We will primarily use the powerful Fiji/ImageJ software, a versatile tool for image analysis. Whether you’re new to the software or looking to enhance your skills, this course is tailored to your needs.

Agenda

All sessions are from 9h30 to 12h00 BST (London, UK time).

• Monday: Introduction to Bio-Image Analysis
  We will explore the basics of image analysis including how to segment and count automatically objects both in 2D or 3D and visualize complex biological structures. One hour lecture then hands-on exercises!
• Tuesday: Filters, Masks and Colocalisation Analysis
  We will dive into image filters and masking for enhanced segmentations, and also cover various types of colocalisation analysis! One hour lecture then hands-on exercises!
• Wednesday: Artificial Intelligence, Image Registration and Particle Tracking
  We will explore techniques that use Artificial Intelligence for image segmentation and image denoising, and image registration and particle tracking. One hour lecture then hands-on exercises!
• Thursday: 1-1 Chats
  In addition to the 1-1 chats we will have on the previous days, we will have dedicated time to talk individually and privately with those of you who want to. You will also have the option to start working on Friday’s automation/macro session.
• Friday: Image Analysis Automation With Macros; Good Practice In Image Analysis
  We will learn how to automate image analysis with scripts so that you can batch-process large datasets. We will also discuss the good and bad practices of image analysis (~ 20 minutes lecture). The macro session will be pre-recorded and the hands-on exercises embedded.

Intended Audience

This course is not just for biologists; it’s for anyone who wants to unlock the potential of quantitative image analysis. While the focus is on fluorescence micrographs of biological samples, the principles you’ll learn are widely applicable across various disciplines. Postgraduate research students and early career researchers will find immense value in this course.

Course Details

• Venue: This course will be conducted online through Microsoft Teams.
• Duration: The course consists of five 2.5-hour live sessions. Depending on your prior experience, you may need additional time to complete the practical exercises.
• Prerequisites: No prior experience is necessary for this course. However, to make the most of it, you’ll need a computer, a stable internet connection and uninterrupted time. A microphone is helpful for interactivity. Having a computer with two monitors can enhance your learning experience.
• Participant numbers: This course is limited to 25 participants.
• Cost: 125 GBP

May 2024 session: more details and sign up here.
(Mon, 6 May – Fri, 10 May 2024)

June 2024 session: more details and sign up here.
(Mon, 17 Jun – Fri, 21 Jun 2024)

Future sessions will also be posted here.

About your instructor

Lior was awarded the University of Oxford’s Early Career Excellent Teacher award and has attained the status of Associate Fellow (AFHEA) in recognition of meeting the UK Professional Standards Framework for teaching and learning support in higher education. He graduated from the University of Oxford with a PhD in Cell Biology. During his PhD and Post Doc, he used advanced imaging techniques and machine-learning enhanced time-lapse live cell imaging.

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