MiCellAnnGELo: annotate microscopy time series of complex cell surfaces with 3D virtual reality

Area of Science:

• * Cell Biology
• * Microscopy
• * Computational Biology

Background:

• * Live cell microscopy generates high-resolution 3D data, revealing previously unobserved biological processes.
• * Manual annotation of 3D microscopy training data is a significant bottleneck for machine learning applications.
• * Existing methods struggle to efficiently process and annotate complex 4D microscopy datasets.

Purpose of the Study:

• * To introduce MiCellAnnGELo, a virtual reality software for immersive 4D microscopy data visualization and annotation.
• * To provide efficient tools for labeling cell surfaces within 3D live cell microscopy datasets.
• * To facilitate the application of machine learning to high-resolution microscopy data.

Main Methods:

• * Development of MiCellAnnGELo using the Unity game engine for cross-platform compatibility (Mac/Unix/Windows).
• * Implementation of an immersive virtual reality environment for interactive data exploration.
• * Integration of specialized tools for precise cell surface annotation in 4D microscopy data.
• * Support for both 2D desktop and 3D VR headset modes.

Main Results:

• * MiCellAnnGELo offers an intuitive platform for annotating 3D microscopy data, overcoming manual annotation challenges.
• * The software enables efficient labeling of cell surfaces, crucial for various biological analyses.
• * Applications include detailed studies of cell motility, endocytosis, and transmembrane signaling.

Conclusions:

• * MiCellAnnGELo significantly improves the efficiency of 3D live cell microscopy data annotation.
• * The virtual reality approach enhances user interaction and data interpretation.
• * This tool empowers researchers to leverage machine learning for advanced biological discovery.