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, James Z. Wang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Automating the three-dimensional (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. 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.

Original languageEnglish (US)
Article number100627
JournalPatterns
Volume3
Issue number12
DOIs
StatePublished - Dec 9 2022

All Science Journal Classification (ASJC) codes

  • General Decision Sciences

Fingerprint

Dive into the research topics of 'Automated 3D segmentation of guard cells enables volumetric analysis of stomatal biomechanics'. Together they form a unique fingerprint.

Cite this