Location: Lincoln University, UK.

This one day interactive training event is designed for early career researchers seeking an understanding about 3D+ microscopy data, to learn about image analysis basics, and/or to gain knowledge to get into biomedical image analysis as a career path. The course focus will be on data from transgenic zebrafish, which are data from previous research projects on developmental biology and drug treatment studies, but the general concepts taught, apply to data from any cell source (e.g. plants, tissue sections).

The workshop is highly interactive, giving participants plenty of time to discuss the work and creating connections. After an initial 1-hour lecture, about 3 hours are dedicated to interactive image analysis training, with regular discussions, reflections, and time for questions. Additionally, there will be time for networking during the lunch break. The afternoon Industry Partner Session will provide insights into applications of imaging and image analysis. The Quick-fire session will be pivotal to brainstorming and discussing ideas. Attendees will work in groups of five to problem-solve their image analysis questions and challenges. This will be facilitated by the trainer, to ensure that all participants will have practical advice they can directly apply to their own data.

Learning objectives

The open-source image analysis Fiji will be used to examine, enhance, segment, and quantify 3D data provided by the tutor. In this training we will examine 3D+ data, use hands-on practical examples to highlight image analysis workflows, and discuss image analysis in its wider implications.

(1) To demonstrate understanding of 3D data and analysis of optical fluorescence microscopy.

(2) To demonstrate common image analysis steps such as data understanding, image enhancement, segmentation, and quantification.

(3) To demonstrate critical analysis of automation, processing of multiple images, automating analysis workflows, and Macros.

Participants will walk away from this course feeling confident in how 3D data are constructed, what common image analysis steps are, and how they could approach their own image analysis project.

Prerequisites

This is an introductory, no-code course focused on using a graphical user interface, so no prior coding experience is necessary. Advanced topics, such as machine learning, will not be covered. Ideally, participants should have some prior experience working with a (fluorescence) microscope to maximize their understanding and engagement in the course content.