Researchers at our institute frequently analyze crowded lung and heart tissue samples in 3D and time lapses. Unfortunately, it is notoriously difficult to segment such samples with sufficient quality using these conventional image-processing workflows and it has turned image analysis into a major bottleneck in our facility. To date, specialized deep learning algorithms like Stardist and Cellpose are the best tools we have found to help our users get the high-quality data they need in a timely, efficient manner. So, I wanted to learn more about it.
The EMBL course “Deep learning for image analysis” provided me with the perfect opportunity to gain an overview over the available tools. It also showed me how to work with them, when to use them, and, most importantly, how to check the validity of the results. It was an intense learning experience and everything about it was geared towards creating the best possible learning environment: the location, the structure of the course, and the team of highly skilled trainers enabled us to train accurate and effective custom models in just a few days.
In the few weeks since the course ended, we already implemented the new knowledge from the workshop in our facility. We automated the analyses for three different projects that, lacking a better alternative, had to be manually quantified before. If needed, we can create simple custom solutions for future projects any time and several deep learning tools for image analysis that are now deployed on our workstations.
The EMBL workshop was a valuable learning opportunity for our imaging platform, and added many new tools to our repertoire that already benefit the facility and our researchers. I deeply thank the MaxBI network for making this possible by covering parts of the travel expenses.
- Kenny Mattonet, Max Planck Institute for Heart and Lung Research -