MiCellAnnGELo

MiCellAnnGELo provides visualization and manual annotation of high-resolution 3D live cell microscopy (4D) datasets to produce labeled cell-surface data for training and refining machine learning models in cellular biology.

Key Features:

• Immersive VR visualization: Enables interactive visualization of 3D microscopy data over time (4D) for detailed inspection of cell surfaces.
• Unity engine implementation: Built using the Unity game engine, providing computational handling of complex microscopy datasets across platforms.
• Annotation tools: Provides tools specifically for labeling cell surfaces in 3D/4D datasets applicable to cell motility, endocytosis, and transmembrane signalling.
• Manual annotation for machine learning: Facilitates generation of manually labeled datasets for training and refining machine learning models.

Scientific Applications:

• Cell Motility: Annotation and analysis of cell shape changes and movement over time from 3D live-cell microscopy.
• Endocytosis: Annotation of membrane internalization events captured in 3D/4D microscopy.
• Transmembrane Signalling: Annotation of membrane-associated signalling processes on 3D cell surfaces.
• Machine Learning Dataset Creation: Production of labeled cell-surface datasets to train computational models in cellular biology.

Methodology:

Built with the Unity game engine and integrated VR visualization to enable manual annotation of 4D microscopy datasets for generation of training data for machine learning models.