A robust and efficient automatic method to segment maize FASGA stained stem cross section images to accurately...

Area of Science:

• Plant biology
• Agricultural science
• Bioinformatics

Background:

• Maize internodes comprise diverse tissues (epidermis, rind, pith, vascular bundles).
• Automated image analysis of grass stem histology is complex and challenging.
• Accurate quantification of histological profiles is crucial for understanding plant development.

Purpose of the Study:

• To develop an automated workflow for detailed maize internode cross-section image segmentation.
• To enable precise quantification of histological variations among different maize genotypes.
• To validate the workflow's applicability across different grass species.

Main Methods:

• Developed a novel image analysis workflow combining pixel color properties (Hue, Saturation, Value) and spatial location.
• Applied the workflow to FASGA-stained maize internode cross-section images.
• Tested the workflow's performance on cross-sections from miscanthus and sorghum.

Main Results:

• Successfully segmented maize internode cross-sections into 40 distinct tissue types.
• The workflow accurately detected subtle histological genotypic variations.
• Demonstrated differential sensitivity of pith tissues to enzymatic digestion.
• Workflow performance was consistent across maize, miscanthus, and sorghum.

Conclusions:

• The automated tool offers high fidelity for analyzing histological profiles.
• It facilitates the identification of maize genotypes with desirable traits.
• The workflow supports research into the genetic basis of histological variations in grasses.