3D segmentation using machine/deep learning
Corbat, Agustin Andres; Vellutini, Bruno;
3D segmentation using machine/deep learning
Lecture about machine and deep learning, oriented to bioimaging and light sheet microscopy. The lecture was prepared for the Light Sheet Image Analysis Workshop 2026. The lecture lasted approximately 40 minutes. The learning outcomes include: Be aware of how machine and deep learning work; Know where to look for models and tools that can be used.
- Max Planck Society Germany
- University of Buenos Aires Argentina
- Universidade Federal de Minas Gerais Brazil
- Max Planck Institute of Molecular Cell Biology and Genetics Germany
light sheet microscopy, machine learning, microscopy, deep learning, bioimage analysis
light sheet microscopy, machine learning, microscopy, deep learning, bioimage analysis
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
selected citations
Citations provided by BIP!
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
Popularity provided by BIP! 0
Average
Average
Average