Automated 3D Segmentation of Guard Cells
Enables Volumetric Analysis of Stomatal Biomechanics

Dolzodmaa Davaasuren, Yintong Chen, Leila Jaafar, Rayna Marshall, Angelica L. Dunham,
Charles T. Anderson and James Z. Wang
The Pennsylvania State University

Abstract:

Automating the 3D segmentation of stomatal guard cells and other confocal microscopy data is extremely challenging due to hardware limitations, hard-to-localize regions, and limited optical resolution. We present a memory-efficient, attention-based, one-stage segmentation neural network for 3D images of stomatal guard cells (3D CellNet). Our model is trained end-to-end and achieved expert-level accuracy while leveraging only eight human-labeled volume images. As a proof-of-concept, we applied our model to 3D confocal data from a cell ablation experiment that tests the "polar stiffening" model of stomatal biomechanics. The resulting data allow us to refine this polar stiffening model. This work presents a comprehensive, automated, computer-based volumetric analysis of fluorescent guard cell images. We anticipate that our model will allow biologists to rapidly test cell mechanics and dynamics and help them identify plants that more efficiently use water, a major limiting factor in global agricultural production and an area of critical concern during climate change.


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Citation: Dolzodmaa Davaasuren, Yintong Chen, Leila Jaafar, Rayna Marshall, Angelica L. Dunham, Charles T. Anderson and James Z. Wang, ``Automated 3D Segmentation of Guard Cells Enables Volumetric Analysis of Stomatal Biomechanics,'' Patterns, vol. 3, article 100627, pp. 1-12, Cell Press, 2022.

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Last Modified: December 10, 2022
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