Abstract

In bioimages, challenging morphologies are often quite hard to segment using traditional computer vision/image analysis methods. Therefore, using semi-supervised machine learning methods like deep learning for such tasks is getting more popular.

About This Material

This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.

Questions this will address

• Why use YOLO for segmentation in bioimage analysis?
• How can I train and use a custom YOLO model for segmentation tasks using Galaxy?

Learning Objectives

• Preprocess images (e.g., contrast enhancement, format conversion) to prepare data for annotation and training
• Perform manual/human-in-the-loop semi-automated object annotation.
• Convert AnyLabeling annotation files into YOLO compatible format for training.
• Train a custom YOLO model for segmentation.