# Characterization of Posidonia Oceanica Seagrass Aerenchyma through Whole   Slide Imaging: A Pilot Study

**Authors:** Olivier Debeir, Justine Allard, Christine Decaestecker, Jean-Pierre, Hermand

arXiv: 1903.03044 · 2019-03-12

## TL;DR

This pilot study employs whole slide imaging and advanced image processing to comprehensively characterize the tissue morphology and aerenchyma structure of Posidonia oceanica seagrass leaves, aiding in understanding their acoustic properties.

## Contribution

It introduces an automated, high-throughput method combining histology, WSI, and machine learning for detailed tissue analysis of seagrass leaves, which was not previously available.

## Key findings

- Detailed cell and void size distributions obtained
- Quantitative tissue density and rigidity metrics derived
- Complete tissue characterization along leaf length achieved

## Abstract

Characterizing the tissue morphology and anatomy of seagrasses is essential to predicting their acoustic behavior. In this pilot study, we use histology techniques and whole slide imaging (WSI) to describe the composition and topology of the aerenchyma of an entire leaf blade in an automatic way combining the advantages of X-ray microtomography and optical microscopy. Paraffin blocks are prepared in such a way that microtome slices contain an arbitrarily large number of cross sections distributed along the full length of a blade. The sample organization in the paraffin block coupled with whole slide image analysis allows high throughput data extraction and an exhaustive characterization along the whole blade length. The core of the work are image processing algorithms that can identify cells and air lacunae (or void) from fiber strand, epidermis, mesophyll and vascular system. A set of specific features is developed to adequately describe the convexity of cells and voids where standard descriptors fail. The features scrutinize the local curvature of the object borders to allow an accurate discrimination between void and cell through machine learning. The algorithm allows to reconstruct the cells and cell membrane features that are relevant to tissue density, compressibility and rigidity. Size distribution of the different cell types and gas spaces, total biomass and total void volume fraction are then extracted from the high resolution slices to provide a complete characterization of the tissue along the leave from its base to the apex.

## Full text

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## Figures

11 figures with captions in the complete paper: https://tomesphere.com/paper/1903.03044/full.md

## References

31 references — full list in the complete paper: https://tomesphere.com/paper/1903.03044/full.md

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Source: https://tomesphere.com/paper/1903.03044