Speakers
Description
This workshop we will show the latest advancements in ilastik, a user-friendly machine learning-based image analysis software that requires no prior machine learning expertise. We will explore how to work with multiscale ome-ngff data (ome-zarr), enhancing ilastik's ability to handle large and complex datasets. Additionally, we will discuss improvements in integration with segmentation tools like cellpose, or stardist, particularly focusing on better support for label image inputs in object classification. Further, we will demonstrate three more advanced workflows of ilastik: Boundary-Based segmentation (Multicut), Trainable Domain Adaptation, and Neural Network Classification including their integration with the deep learning tools of the BioImage Model Zoo. Finally, we want to show recent developments on integration of ilastik with Python image analysis workflows.
| Target audience | Beginner users, Intermediate users, Beginner developers, Intermediate developers |
|---|---|
| Keywords | machine learning, deep learning, ai, image segmentation, image processing, object classification, ngff |
