Séminaire : Hugues Talbot

Image analysis is the process of extracting meaningful information from image data. In the last 10 years or so, this process has changed from being a specialised domain requiring advanced knowledge of mathematical low-level vision operators, to a subdomain of Artificial Intelligence dominated by machine-learning and particularly deep-learning methods. These use data and examples to learn how to do a task, such as image classification, instead of following fixed rules. They can produce impressive results, but they also have some challenges.

One challenge is getting enough data, especially in 3D, which are harder to obtain than 2D images, because they need special equipment and techniques. Another challenge is getting expert labels for the data, which means telling the machine what each part of the image shows. This is essential, but it can be very hard and time-consuming to do, especially in 3D. Some tasks are even harder than image classification, such as image segmentation. Image segmentation is a task that involves dividing an image into regions that belong to different objects or categories, such as organs or tissues in a medical image. This task requires more detailed labels than image classification, which makes it more difficult for machine learning and deep learning.

In this talk, we will explain some of the recent methods that use deep learning for image segmentation in 3D. We will not assume prior knowledge of machine learning or image processing. We will show some of the basic concepts and show some useful tools to perform image analysis of 3D images.