# rhizoscan: an automated image-processing pipeline labex ......labex numev solutions numériques...

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LABEX

NUMEVSolutions

NumériquesMatérielles et

Modélisation pourl’Environnement

et le Vivant

RhizoScan: An automated image-processing pipeline for high-throughput analysis of root architecture in OpenAlea

(1) Virtual Plants, INRIA-CIRAD-INRA, Montpellier, France(2) BPMP, INRA, Monpellier, France(3) DAR, CIRAD, Montpellier, France

(4) Zenith, INRIA-LIRMM-UM2-CNRS, Montpellier, France(5) Earth and Life Institute, Louvain-la-Neuve, Belgique(6) LEPSE, SPIC, INRA, Montpellier, France

Positionnement NUMEV: Axes Modélisation, Algorithmes et calculs, Données scientifiques

contact : [email protected]

Mots clés : Architecture racinaire, Analyse d'images, haut-débit, OpenAlea

ABSTRACT:AutomatedacquisitionsystemsofRootSystemArchitecture(RSA)arenowreadilyavailablefordevelopmentalresearchandprovidehigh‐throughput imagedata.Existingacquisitionsystemsprovidemanytypesofdata, fromimagesofdispersedrootpiecestofull3dscansofundergroundrootsystems.Hereweconsideratraditionalexperimentalprotocolusedforhigh‐throughputimageacquisition:imageofrootgrown inpetriplatesacquiredusingeither a scanneror adigital camera. Their analysis is thusamajor challenge for researcheson rootdevelopment.ThegoalofthepresentedworkistoprovideananalysispipelinetoautomaticallyprocessexperimentaldataandextracttheimagedRSA.Becauseour framework is included in theOpenAleaplatform, theextracteddata canbeused seamlessly as inputof all thearchitecturalanalysispackagesalreadycontainedinOpenAlea.

Rootarchitecturecomplexityandsystemautomation

Dart(4)

RootTrace(9)RootLM(8)KineRoot(7)

RootReader2D(6)

EZ‐Rhizo(5)

SmartRoots(1)

Complexity

LevelofAutomation

Rhizos

can

WinRhizo(3)

RootNav(2)Extractgraph

EstimateRSAtree

segmentimage

Segmented image area

Graph ofroot segments

Image segmentation:Images are segmented into three areas: - the petri plate - the seed and leaves, which are treated as the start of the root architecture - the pixel area containing root axes

An initial non-uniform background lighting is estimated based on minimum pixel intensity with large-scale distribution. It is then removed from the original images. The segmentation is then done using the Expectation Maximization algorithm on pixel luminosity.

Root graph extraction:The segmented root pixels are clustered into root sections between branching and crossing using a skeletization algorithm. A 1st order spline (polyline) is then fitted on each such curve of the skeleton.

The root graph is constructed with the set of the skeleton and polyline nodes, and with the polyline segments. At this stage, the graphs are not tree structures as they usually contain loops.

Estimation of the RSA tree:The root architecture is obtained by converting the root graph to an axial tree structure. First the graph is converted to a directed acyclic graph (DAG). A set of paths rooted at the detected seeds and which cover the DAG is then constructed from a minimum spanning tree computed over the angles between root segments. Finally a model based selection of root axes is used to construct the RSA tree from those paths. In addition, a path merging algorithm is applied to cope with superposition of root axes in the graph.

Root SystemArchitecture

agropolis fonda�on

(1) G. Lobet et al, 2011 Plant Physiology(2) M. Pound et al, 2013, Plant Physiology(3) WinRhizo-pro, 2012. Regent Instruments Inc.(4) J. Le Bot et al, 2010, Plant and Soil, pp. 261(5) P. Armengaud et al, 2009 The Plant Journal(6) www.plantmineralnutrition.net/rootreader.htm(7) P. Basu et al, 2007, Plant Physiology(8) X. Qi et al, digital.cs.usu.edu/~xqi/RootLM/(9) A. French et al, 2009, Plant physiology

RhizoScaninOpenAlea

Withoutsuperposition

Withsuperposition

J. Diener(1,4), P. Nacry(2), C. Périn(3), A.Joly(4), F. Masseglia(4), A. Dievart(3), X. Draye(5),F. Boudon(1), A. Gojon(2), H. Sentenac(2), B. Muller(6), C. Pradal(1), C. Godin(1)

Rhizoscan dataflow in VisuAlea

RESULTS:The comparison was done on 600 root systems (200 for the first method) from an Arabidopsis Thaliana data set over a six day period. Each dot in the below plots represents an automatic measurement (y-axis) with respect to its associated reference measurement (x-axis) of one plant. The red lines indicate perfect match.

We can see that the automatic measurement of the primary axes length fits well to the reference data with both approaches.The secondary root length and, by concequence, the total root length, show a sub-estimation bias with the first method that is proportional to the root complexity. This has been explained by the inability to find root superpositions. The second method which now allows such superposition provides centered measurement estimates.

To quantify the accuracy of our reconstruction method, reference data were obtained by manual annotation of the analysed root systems. Experts manually specified the root architectures on a series of images. Then, selected measurements of root axes length and number were made on these reference root systems and compared with those made on the structures automatically reconstructed with our image analysis pipeline.

We present two sets of comparisons:

A first set was obtained with an initial algorithm for the RSA tree estimation step that could process root crossing aloneThe second set was obtained with our current algorithm that was designed to cope also with root superposition

The main limitation in the presented method is the difficulty to select the correct path on the root graph that represent root axes. This difficulty applies in particular to the path merging algorithm used to cope with root superposition, and it is proportional to the root system complexity. We are currently working on a root tracking method that uses RSA trees estimated at previous time steps when analysing later root systems. By propagating this information the path selection problem will be reduced at each time step to only the axes growth and new emergent axes.

Current work:

Rhizoscan root editor

METHOD: