Neuroscience Image Analysis

Event Notes:

Course Overview

This two-part online training course is aimed at ECRs and students who want to enhance their image analysis skills and explore innovative solutions for complex tasks in the field. The course is structured to guide participants from beginner to proficient user.

The training course consists of two half day interactive, live online sessions.

What will the course cover?

Session 1: Tuesday 11 March 

The first session will focus on fundamental concepts and practical applications and equip participants with the foundational skills required to address common image analysis tasks using ImageJ/Fiji.

Session 2:  Thursday 13 March 

Building on the concepts and applications covered in Session 1, this session will cover the complete workflow from imaging acquisition to analysis.  It will introduce participants to specialised neuroscience tools and advanced commercial software, addressing challenges such as fractal analysis, skeletonisation, Sholl analysis, and neuron characterisation.

Additionally, the session will provide a brief overview of cutting-edge neuroscience tracing tools, including the Imaris artificial intelligence identification system and Arivis deep learning models.

Course Leader

Session 1: Tuesday 11 March 

The first session will focus on fundamental concepts and practical applications and equip participants with the foundational skills required to address common image analysis tasks using ImageJ/Fiji.

Session 2:  Thursday 13 March 

Building on the concepts and applications covered in Session 1, this session will cover the complete workflow from imaging acquisition to analysis.  It will introduce participants to specialised neuroscience tools and advanced commercial software, addressing challenges such as fractal analysis, skeletonisation, Sholl analysis, and neuron characterisation.

Additionally, the session will provide a brief overview of cutting-edge neuroscience tracing tools, including the Imaris artificial intelligence identification system and Arivis deep learning models.