Introduction to nuclei segmentation with StarDist - [NEUBIASAcademy@Home] Webinar

Materials and Slides
github.com/maw...
Video Outline:
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Introduction to deep learning: 06:00
The problem of nuclei segmentation: 10:01
StarDist principle: 13:33
Accuracy measures: 20:16
Example StarDist results: 23:40
StarDist in 3D: 23:40
QA session I: 26:41
How to use StarDist: 29:44
Training custom models: 39:50
Collaboratory demo: 44:46
Getting help with StarDist: 57:05
StarDist in a Core Facility: 58:00
User Projects: 01:07:21
QA session II: 01:16:11
Contents:
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This webinar gives participants an overview of StarDist, a deep learning based method for segmentation of roundish objects (such as nuclei) in densely packed and noisy 2D or 3D microscopy images.
Learning Outcomes:
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After this webinar, the participants should be able to
- Know if StarDist would be suitable for their data
- Create appropriate training data
- Prevent common training mistakes
Speakers:
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Dr. Martin Weigert, group leader at EPFL Lausanne, where his research focuses on machine learning based image reconstruction and segmentation, as well as computational microscopy.
Olivier Burri, senior bioimage analyst at the BioImaging & Optics Core Facility (EPFL - SV - BIOP)
Moderators:
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Dr Uwe Schmidt, postdoc at MPI-CBG in Dresden, Germany. His research interests include machine learning and computer vision.
Dr. Siân Culley, postdoc at the LMCB at UCL, UK. Her research interests are developing open source image analysis and applying this to problems in cell biology.
Dr. Daniel Sage, specialist of open-source working at the EPFL Biomedical Imaging Group
Ofra Golani, bioimage analyst at Weizmann Institute.
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All Questions & Answers are available on the image.sc forum, where you can further ask questions in the future:
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