# Quantitative imaging of plants: multi-scale data for better plant anatomy

David Legland, Marie Francoise Devaux, Fabienne Guillon

► To cite this version:

David Legland, Marie Francoise Devaux, Fabienne Guillon. Quantitative imaging of plants: multi-scale data for better plant anatomy. Journal of Experimental Botany, 2018, 69 (3), pp.343-347. 10.1093/jxb/erx416. hal-02625043

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<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
species to better follow the mechanisms controlling seed pro-
duction under changeable environments, which will be a con-
tinuing trait of importance for sustainable grain production
of our most important crops.

Keywords: Fertilization, flower, ovary, pericarp, pollination, wheat.

Journal of Experimental Botany, Vol. 69, No. 3 pp. 341-343, 2018
doi:10.1093/jxb/erx430

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Insight

Quantitative imaging of plants: multi-scale data for better
plant anatomy

David Legland*, Marie-Françoise Devaux and Fabienne Guillon

UR1268 Biopolymères. Interactions et Assemblages, INRA, France

* Correspondence: david.legland@inra.fr

The ongoing development of imaging systems con-
tinuously brings novel possibilities for the exploration
of plant anatomy at different scales. However, increas-
ing resolution often results in a smaller field of view,
limiting the scope for wider conclusions. Staedler
et al. (2018) got round this problem by making use of
3D images acquired at two different scales to esti-
mate the number of pollen grains within flowers. It is a
powerful approach, providing much more information
than with a single scale.

An understanding of the biological functions, develop-
ment, or evolution of plants requires an accurate descrip-
tion of their anatomy at various scales: the whole organism,
its organs, tissues within each organ, cells within a tissue,
the cell walls, or the organelles within a cell. Depending on
the representative scale of the structures of interest, various
image acquisition devices can be employed to investigate their
morphology, chemical composition, or spatial organization
(Rousseau et al., 2015) (see Box 1).

Historically, microscopy has been the usual technique for
investigating plant anatomy at the cellular or tissue scale, and
the rise of confocal microscopy has allowed us to perceive

the 3D structure of tissues or organs with a resolution at
the micron level (Truernit et al., 2008). But new technolo-
gies – such as the recent development of super-resolution
techniques (e.g. PALM or STORM) or the introduction of
optical coherence tomography (OCT) (Lee et al., 2006) –
continuously bring novel imaging possibilities. For imaging
cell walls or organelles within the cells, electron microscopy
has often been the method of choice, reaching resolutions at
the nanometre scale. The 3D structure can also be assessed,
either by combining scanning electron microscopy with ser-
ial sectioning of the specimen (Bhawana et al., 2014), or
by adapting tomography algorithms to transmission elec-
tron microscopy. Magnetic resonance imaging (MRI) and
X-ray computed tomography are popular methods for the
non-destructive investigation of the 3D architecture of bio-
logical specimens, without the need for staining, section-
ing or inclusion. The high resolution reached by computed
tomography (below the micron) often makes it the best
method for the investigation of plant organs (Stuppy et al.,
2003; Cloetens et al., 2006; Dhondt et al., 2010; Staedler
et al., 2013). Staedler et al. (2018) took advantage of this
resolution to quantify the 3D anatomy of orchid inflores-
cences, and through this showed differences in reproductive
Box 1. Multi-scale imaging of wheat grain

The anatomical structure of plants can be assessed at different scales. At the largest scale, the whole plant can be imaged using digital photography. The structure is commonly quantified by phenotypic feature related to the size (e.g. length, volume, thickness) of the different organs. The spatial localization of the organs (e.g. position of leaves or fruits on the stem) or their organization may also be relevant. When focusing on a specific organ, a common question is how the different compartments are organized relative to each other. Imaging modalities such as tomography (A) or magnetic resonance imaging (MRI) allow for non-destructive investigation of the global organ geometry (e.g. size, shape, curvature) and organization of different compartments (computed tomography image, A, courtesy of C. Girousse, INRA Clermont-Ferrand). The slicing and staining of samples, followed by macroscopy or microscopy imaging, provides more precise information on the spatial organization of the tissues within an organ (B). When the resolution is sufficient, the size and shape of the cells within tissues may also be assessed (C). At a larger magnification, the use of electron microscopy (D) gives access to more detailed information, such as the morphology of cell walls (e.g. thickness, curvature). Using adequate staining or immuno-labelling also makes it possible to identify specific chemicals, and to describe their spatial localization.

<figure><img src="image_2.png" /></figure>

investment between inflorescences of rewarding and deceiv-
ing orchids.

The physical properties of image acquisition devices limit the total quantity of information that can be gained, and so there is a compromise between a high resolution and a large field of view. When the spatial resolution is too low, the smallest structures are difficult to identify. On the other hand, the smaller the field of view, the more difficult it is to cover the totality of the organ of interest with a reasonable acquisition time. The increase in resolution of reconstructed images therefore often corresponds to a reduction in the size of the field of view. This difficulty was encountered in the work of Staedler et al. (2018): the structures of interest (pollen grains) could not be imaged with a resolution that allowed their identification while taking into account the whole reference structure (the pollinium, an aggregate of pollen grains). The strategy adopted to circumvent this difficulty was to acquire images at two different resolutions. Images acquired at finer resolution were used for segmentation and counting the pollen grains; images at a coarser resolution were used for assessing the size and shape of the reference structure. The total number of pollen grains was then estimated by combining their numerical density with the volume of the pollinium. It is an approach which exemplifies how data obtained at different

resolutions may be used together to provide much more infor-
mation than data at a single resolution.

The multi-scale investigation of plant tissues is undoubtedly a promising strategy for a better description and understanding of plant anatomy. However, investigation and integration of images, obtained both at different scales and using different imaging modalities (see below), raise new methodological questions (Rousseau *et al.*, 2015).

Joint exploration of multi-scale images

Investigating plant anatomy at different scales often relies on different imaging modalities. A common approach in microscopy for combining these modalities is correlative microscopy, in particular correlative light and electron microscopy (CLEM) (Bell et al., 2013), and this correlative approach can also be performed with other modalities. For example, the joint analysis of 3D modalities allows the investigation of both anatomy and physiology (Jahnke et al., 2009). Similarly, hyperspectral images obtained from different spectroscopic techniques can be coupled (Allouche et al., 2012). Quantification of the cellular morphology of tomato pericarp was also performed from images obtained using both
Box 2. Quantitative data from plant organs using multi-scale imaging

Digital images may be processed and analysed to provide quantitative information. However, comparison and integration of quantitative information measured on images obtained at different scales are not always straightforward. In the example shown, images of tomato pericarp have been produced using two acquisition devices. The macroscopy imaging of a pericarp slice allows different tissues to be distinguished (e.g. epidermis, vascular bundles, parenchyma) as well as variations of cell morphology dependent on their location. However, it is difficult to individualize cells due to sampling resolution and the superposition of cell layers.

The cellular morphology within images was quantified using texture analysis tools, measuring variations in shades of grey, and enabling an assessment of mean size variation dependent on the distance to the outer epidermis. A set of 3D confocal laser scanning microscopy images was also acquired along the pericarp and stitched together. The cellular morphology was then quantified by estimating specific cell wall surface area. Assuming that cells are spherical, a typical cell diameter can be estimated, as well as its variation dependent on the distance to the outer epidermis. A comparison of estimated cell diameter obtained from both imaging methods reveals that relative variations are very similar, but that absolute values differ with a scaling factor equal to two or three. The imaging modality at larger scale (here the macroscopy) provides more integrated data, resulting in smoother profiles. The imaging modality with better resolution (here the microscopy) exhibits stronger variability, due to the smaller size of the field of view. The scaling difference between the two profiles can be interpreted as the difference in the resolutions: finer imaging resolves more details, resulting in a smaller estimation of typical cell size.

<figure><img src="image_3.png" /></figure>

macroscopy and microscopy imaging, applying a statistical integration approach (Legland et al., 2012). Finally, the multi-scale strategy can be employed for modelling purposes (Mendoza et al., 2007).

In many cases, for example to evaluate the quality of acquisitions, it is of interest to visualize, simultaneously, all the images obtained at different scales on the same sample. Unfortunately, the management and visualization of multiple images obtained with different resolution and/or different orientation still remain complicated. The spatial alignment of two different views of the same object is performed by applying image registration algorithms, which automatically identify the geometric transformation mapping one image onto another (Zitova and Flusser, 2003). Many algorithms have been developed, mostly in the context of medical imaging. The registration of images at similar scales is possible, but is difficult to apply in an automated way when differences

in scales are large. Few user-friendly software solutions take into account spatial positioning of images for visualization.

Fusion of multi-scale quantitative data

Using high-resolution 2D and 3D imaging at the tissue level makes it possible to quantify the 2D and 3D morphology and/or spatial organization of whole biological structures. However, contrary to conventional 2D imaging and except for simple tasks such as counting objects, performing manual measurements on 3D images is challenging – the quantification of anatomical structures therefore strongly relies on adequate image processing and analysis pipelines. The 3D raw images are usually converted into 3D reconstructions of the structures as binary images or geometrical models, and the reconstructed geometry then quantified using adequate
descriptors. In the simpler case, structures or structural sec-
tions can just be counted. When the structures of interest
can be delimited, either manually or by the use of segmen-
tation methods, their geometry can be quantified (e.g. vol-
ume, surface area, thickness). When the structure of interest
consists of a collection of elements that can be ‘individual-
ized’ (e.g. cells, pores), its geometry can also be described
by the shape or size distribution of its elements. In some
cases, more complicated descriptors may be envisioned,
for example based on skeletization of the microstructure
(Mendoza et al., 2007).

Estimating a global quantity from quantifications per-
formed in fields of view with limited size requires a statistical
approach. As in Staedler et al. (2018), the field of view may
be assumed to be representative of the whole organ or tissue
under investigation. For some plants, however, tissues may
present large variability in morphology depending on their
position in the organs. For example, the morphology of cells
in fleshy fruit pericarps varies significantly depending on the
distance to the outer epidermis. In such cases, an adequate
sampling strategy has to be performed, either to integrate the
biological variability or to quantify position-dependent vari-
ations in morphology.

Another methodological question relates to the differ-
ences in resolution of 3D images, which affect the precision
and accuracy of measurements taken from them (Arganda-
Carreras and Andrey, 2017). When measuring the volume of
a biological structure, the estimated values converge when
the resolution increases. Increasing the resolution increases
the number of details which can be detected and quanti-
fied, as well as how many small objects can be distinguished.
Contrary to measurement of volume, the measurement of
surface area from 3D images thus increases with image reso-
lution, as smaller surface variations are detected. This effect
was observed, for example, by Chevallier et al. (2014) on
food products, with micro-tomography using two different
scales. A larger quantity of fine structures (within the over-
all distribution of structure size) was observed with a smaller
voxel size. In plant sciences, a similar effect was observed by
Legland et al. (2012) in quantifying the morphology of cells
in tomato pericarp using two different imaging modalities.
The average cell size estimated from macroscopy images was
larger than the one obtained after estimation from 3D con-
focal microscopy (see Box 2).

Computational challenges related to
multi-scale imaging

The management of image data obtained at different scales also leads to new computational issues, many of which are reviewed in Walter *et al.* (2010). A first issue is access to data. The large amount of image data, especially when multi-dimensional (3D, time-lapse, multi-channel), is not always well managed by current image-processing software (although specific software and file formats have been developed for accessing high-resolution images produced by slide scanners, using a pyramidal approach). The heterogeneity of

specific or proprietary file formats may also restrict accessibil-
ity to the data or its reusability.

With the increase in amount of image data, visualization becomes more complicated. Many different projection or rendering methods can be used for exploring multi-dimensional data sets, but the superposition of images obtained from different modalities increases the complexity of the task. In the case of multi- or hyperspectral images, vector spectral data are associated with an image element. Statistical methods such as principal components analyses are therefore necessary to be able to extract relevant information, and to represent its spatial variations within the plant or organ (Geladi and Grahn, 1996).

Quantitative image analysis usually requires the process-
ing of large collections of images to identify relevant fac-
tors related to changes in morphology or organization. The
proper management of meta-data associated with images
obtained on different individuals, with different imaging
modalities or at different scales, requires organization and
a rigorous approach. Nevertheless, new software solutions
such as OMERO (Goldberg et al., 2005) have emerged for the
management of large image collections.

To conclude, while modern imaging techniques allow the
investigation of plant anatomy at different scales, many chal-
lenges still exist for the quantification of data from complex
imaging modalities and the fusion of data obtained from dif-
ferent scales, modalities, or datasets.

Keywords: Image analysis, image fusion, image registration, multi-scale imaging, quantitative histology.

Journal of Experimental Botany, Vol. 69, No. 3 pp. 343-347, 2018
doi:10.1093/jxb/erx416

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