As part of the Imaging ONEWORLD series, the focus of these lectures is on microscopy and image analysis methods and how to apply these to your research. Almost all aspects of imaging such as sample preparation, labelling strategies, experimental workflows, ‘how-to’ image and analyse, as well as facilitating collaborations and inspiring new scientific ideas will be covered. Speakers will be available for questions and answers. The organisers, core facility staff from the University of Cambridge, Gurdon Institute, MRC-LMB and the ICR/Royal Marsden Trust are also able to continue the discussion and provide advice on your imaging projects.

Scientific Organisers


Machine learning for microscopy with less training data

Deep learning-based approaches are revolutionizing imaging-driven scientific research. In image reconstruction and classification, in segmentation and artificial labelling, they have pushed both flagship projects and bread-and-butter everyday tasks, allowing image analysis to keep pace with the recent advancements in imaging technology and instrumentation. I will talk about the recent work of my group on large-scale segmentation and exploration of cells as well as our current efforts to reduce the annotation budget for training of segmentation algorithms. I will also show how we strive to make our methods accessible to biologists without computational expertise through our software ilastik and through the emerging community network collection at the BioImage Model Zoo.