Title: AI-based image segmentation and labeling with free open source software, 3D Slicer

Description:

This tutorial aims to help new, intermediate and seasoned 3D Slicer users alike to harness the various image segmentation and annotation tools effectively, and in doing so open up the capabilities of AI-assisted medical image computing technologies to a wider audience. Importantly, considering that this being the first MICCAI conference on the African continent, our tutorial is intended to align with this year’s wider objective of low-cost medical image computing and computer assisted interventions.

Attendees will start by learning about the objectives and impact of 3D Slicer through a plenary talk delivered by an invited speaker. This will be followed by a brief introductory session to segmentation within 3D Slicer, during which attendees will follow along on their laptops and learn how to use built-in tools to segment sample data. Next we will explore AI-based segmentation tools and teach attendees how to deploy segmentation models on their own computers. Finally, the tutorial will close with a hands-on demonstration session in which attendees will have the opportunity to see and use clinical research systems that integrate segmentation and AI. To maximize the utility and interest of the hands-on demonstration portion of the tutorial, attendees will be asked to vote on possible topics during workshop registration.

The core learning outcomes of the tutorial will include:

• How to load and visualize data using 3D Slicer
• How to use various built-in and AI-based segmentation tools to annotate data
• How to utilize annotated data and segmentation tools within clinical applications of interest