Mapping 4D cell models with VR

Four-dimensional imaging of cotyledon pavement cells with Aivia artificial intelligence software

4D morphological information of Arabidopsis thaliana cotyledon pavement cells mapped using both direct water immersion observations and computational image analyses, including segmentation, surface modeling, virtual reality and morphometry. Arabidopsis-thaliana-cotyle.jpg

In the world of plant research, the way pavement cells in the outermost epidermal layer of the leaves of dicotyledonous plants develop during leaf expansion morphogenesis is still not fully understood. These pavement cells, which form a protective outer layer for more specialized cells below, exhibit complex jigsaw-like patterns. In an article published in the journal Plant Biotechnology, researchers Takumi Higaki and Hidenobu Mizuno demonstrate how to analyze three-dimensional shapes of pavement cells over time, i.e., four-dimensional data, when studying the relationship between mechanical modeling and simulations and the actual shape of the cells. They have developed a framework to capture and analyze 4D data with the help of artificial intelligence image analysis software Aivia, which allows researchers to use virtual reality (VR) equipment to map and explore the inner workings of cells. The researchers were able to perform a time lapse 3D morphometrical analysis with detailed mapping of cell growth rate and shape changes over time. 

Read the full article:

T. Higaki, H. Mizuno:

Four-dimensional imaging with virtual reality to quantitatively explore jigsaw puzzle-like morphogenesis of Arabidopsis cotyledon pavement cells

Plant Biotechnology (2020) vol. 37, no. 4, pp. 429-435, DOI: 10.5511/plantbiotechnology.20.0605a.

I used to examine my 3D cell models by looking at a 2D monitor, but with Aivia VR, I can intuitively control my viewpoint and the position of the model. It also reduces my observation load, allowing for a more immersive observation. Aivia VR is an indispensable tool for me to improve the quality of my data observations.

Takumi Higaki, Faculty of Advanced Science and Technology (FAST), Kumamoto University

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