This work was performed in the Environmental Molecular Sciences Laboratory, a national scientific user facility sponsored by the Department of Energy's Office of Biological and Environmental Research and located at Pacific Northwest National Laboratory.

Caution: The operation of an X-ray tomography scanner requires both general radiation training, and instrument-specific radiation safety training. All corresponding procedures relevant to the experimenter&#39;s laboratory should be followed.&#160;
1. Root Imaging
Note: This step describes the imaging of a grass specimen held in its original soil in a tubular plastic pot (a plastic tube with a diameter of 40 mm, a height of 210 mm, and wall thickness of about 2 mm).
<ol>
	<li>Place potted plant on the sample manipulator of the instrument at a distance desired for target magnification. For a plant in a 2-inch diameter holder, the sample to source distance should be about 3 inches (7 cm).</li>
	<li>Adjust the X-ray scan settings to achieve optimal color (grey level) contrast in detector image. Note: These settings are available in the instrument control software used.
	<ol>
		<li>Set X-ray power settings; 85 kV and 190 &#956;A were used in this example.</li>
		<li>Set exposure time. Here, a relatively long exposure time of 1 sec was used for better signal-to-noise ratio.</li>
		<li>Set number of projections and frames per projection; 4 frames per projection for a total of 3,142 projections is suggested for good data statistics.</li>
		<li>Run a shading correction using the measurement conditions set above by selecting the &#34;Shading correction&#34; tab, and clicking &#34;Create&#34;. 
		Note: The shading correction compensates for the variation in response of the imaging device&#39;s pixels when illuminated with a constant set of X-ray flux. The process takes blank images (with sample removed from beam path) with the X-ray beam turned on, and with the beam turned off. This correction is applied to all images collected.</li>
		<li>Select the &#34;Minimize ring artefacts&#34; option (also called &#34;shuttling mode&#34;); the sample will be rotated in angular steps whilst the projection images are acquired. This leads to data acquisition at a slower rate, but helps eliminate ring artefacts.</li>
		<li>Start the scan by clicking the &#34;Acquire&#34; button under the Acquisition tab (with the settings outlined above, image collection will take approximately 4 hr).</li>
	</ol>
	</li>
</ol>
2. Data Reconstruction&#160;
Note: This section describes the reconstruction of 3D volume data from the raw images (radiographs from the CT scan).
<ol>
	<li>Load the raw data into the program.</li>
	<li>Compare first and last image (they should be nearly identical as the last image is taken after a 360o rotation of the specimen) to make sure the specimen did not move or the scan settings did not change during data acquisition.</li>
	<li>Calculate the center of rotation (COR) by selecting the &#34;Center of rotation&#34; tab, and clicking &#34;Start&#34;; use options &#34;Automatic&#34; COR finding with &#34;High Quality&#34; accuracy, and &#34;Dual&#34; (upper and lower) slice selection for COR calculation.</li>
	<li>Select the sample volume to be reconstructed: select the &#34;Volume&#34; tab, and edit the volume selection windows using the thumbnails.</li>
	<li>Perform reconstruction to create the volume file containing 3D data by clicking &#34;Start&#34;.</li>
</ol>
3. Data Processing / Segmentation
Note: This section describes the steps to be taken to prepare the reconstructed data for further processing in the program RooTrak to track roots as they branch out through soil, and isolate the roots from any surrounding material to produce a stack of binary images of just the root itself.
<ol>
	<li>Processing of volume data in ImageJ to prepare a RooTrak processable image stack:
	<ol>
		<li>Load the volume file into ImageJ.</li>
		<li>Optimize image contrast between root and soil by adjusting brightness and contrast settings (Click Image/Adjust/Brightness/Contrast). When the region of interest within the image is visible and clearly distinguishable, the settings are considered optimized.</li>
		<li>Save as an image stack in jpeg, bmp, or png format.</li>
	</ol>
	</li>
	<li>Processing in RooTrak to segment the root:
	<ol>
		<li>Load image stack into RooTrak (go to &#34;Tools&#34; tab, and press &#34;Tracker&#34;).</li>
		<li>Set seed points inside root: click several points inside each of the pertinent root sections visible in the top view slice of the volume data.</li>
		<li>Set tracker parameters &#34;Smoothness&#34; and &#34;Similarity&#34; to 0.3 and 0.8, respectively.</li>
		<li>Run the tracking function. This will follow the root from the top image slice all the way to the bottom slice.</li>
		<li>After viewing the volume data, select the number of slices according to the useable data volume; in this case, tracking was stopped at 200 slices, equivalent to a depth of 6.2 mm, where root boundaries became ill-defined (the image of the tracked root started to blend into that of the soil). 
		Note: The image stack produced will be saved automatically to wherever the output directory was created.</li>
	</ol>
	</li>
</ol>
4. Volume and Surface Analysis
Note: This step describes the isosurface mesh generation from the image stack created by RooTrak.
<ol>
	<li>Convert image stack from RooTrak into a binary image format in ImageJ. Select &#34;Process&#34;, then &#34;Binary&#34;, then &#34;Make Binary&#34;.</li>
	<li>Use the open-source ImageJ plugin, BoneJ, to create the triangular mesh; in ImageJ select &#34;Plugins&#34;, then &#34;BoneJ&#34;, then &#34;Isosurface&#34;.</li>
	<li>Set &#34;Resampling&#34; and &#34;Threshold&#34; to 6 and 120, respectively (Default settings). Check &#34;Show surface&#34;, and press the &#34;OK&#34; button.</li>
	<li>On the &#34;3D viewer&#34; click on the File tab, then on &#34;Export surfaces&#34;, then save as &#34;STL (binary)&#34;.</li>
	<li>Open imeshJ, select the STL file and enter voxel size in microns. Click &#34;Calculate Surface Area&#34; to acquire total sample root surface area in mm2. Similarly, click &#34;Calculate Volume&#34; to obtain total sample root volume in mm3.</li>
</ol>

<ol>
	<li>McKenzie, B. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Rhizosphere:+An+Ecological+Perspective.">The Rhizosphere: An Ecological Perspective.</a> Eur. J. Soil Sci. 59 (2), 416-417 (2008).</li><li>Farrar, J., Hawes, M., Jones, D., Lindow, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+roots+control+the+flux+of+carbon+to+the+rhizosphere.">How roots control the flux of carbon to the rhizosphere.</a> Ecology. 84 (4), 827-837 (2003).</li><li>Gregory, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Roots+rhizosphere+and+soil:+the+route+to+a+better+understanding+of+soil+science?.">Roots rhizosphere and soil: the route to a better understanding of soil science?.</a> Eur. J. Soil Sci. 57 (1), 2-12 (2006).</li><li>Philippot, L., Raaijmakers, J. M., Lemanceau, P., van der Putten, W. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Going+back+to+the+roots:+the+microbial+ecology+of+the+rhizosphere.">Going back to the roots: the microbial ecology of the rhizosphere.</a> Nat. Rev. Microbiol. 11 (11), 789-799 (2013).</li><li>Gregory, P. J., Hutchison, D. J., Read, D. B., Jenneson, P. M., Gilboy, W. B., Morton, E. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Non-invasive+imaging+of+roots+with+high+resolution+X-ray+micro-tomography.">Non-invasive imaging of roots with high resolution X-ray micro-tomography.</a> Plant and Soil. 255 (1), 351-359 (2003).</li><li>Mairhofer, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RooTrak:+Automated+Recovery+of+Three-Dimensional+Plant+Root+Architecture+in+Soil+from+X-Ray+Microcomputed+Tomography+Images+Using+Visual+Tracking.">RooTrak: Automated Recovery of Three-Dimensional Plant Root Architecture in Soil from X-Ray Microcomputed Tomography Images Using Visual Tracking.</a> Plant Physiol. 158 (2), 561-569 (2012).</li><li>Anderson, S. H., Hopmans, J. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Soil-Water-Root Processes: Advances in Tomography and Imaging. , (2013).</li><li>Mairhofer, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Recovering+complete+plant+root+system+architectures+from+soil+via+X-ray+mu-Computed+Tomography.">Recovering complete plant root system architectures from soil via X-ray mu-Computed Tomography.</a> Plant Methods. 9, 8 (2013).</li><li>Lorensen, W. E., Cline, H. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Marching+cubes:+a+high+resolution+3D+surface+construction+algorithm.">Marching cubes: a high resolution 3D surface construction algorithm.</a> Comput. Graph. 21 (4), 163-169 (1987).</li><li>Lobet, G., Draye, X., Perilleux, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+online+database+for+plant+image+analysis+software+tools.">An online database for plant image analysis software tools.</a> Plant Methods. 9 (38), (2013).</li><li>Schmidt, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+iRoCS+Toolbox+-+3D+analysis+of+the+plant+root+apical+meristem+at+cellular+resolution.">The iRoCS Toolbox - 3D analysis of the plant root apical meristem at cellular resolution.</a> Plant J. 77 (5), 806-814 (2014).</li><li>Galkovskyi, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GiA+Roots:+software+for+the+high+throughput+analysis+of+plant+root+system+architecture.">GiA Roots: software for the high throughput analysis of plant root system architecture.</a> BMC Plant Biol. 12, 116 (2012).</li><li>Clark, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=3-Dimensional+Root+Phenotyping+with+a+Novel+Imaging+and+Software+Platform.">3-Dimensional Root Phenotyping with a Novel Imaging and Software Platform.</a> Plant Physiol. 156, 455-465 (2011).</li><li>. RootSnap! Available from: <a target="_blank" href="https://www.cid-inc.com">https://www.cid-inc.com</a> (2013)</li><li>Arsenault, J. L., Pouleur, S., Messier, C., Guay, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=WinRHIZO&#8482;+a+root-measuring+system+with+a+unique+overlap+correction+method.">WinRHIZO&#8482; a root-measuring system with a unique overlap correction method.</a> HortSci. 30, 906-906 (1995).</li></ol>

We have nothing to disclose.

A combination of X-ray computed tomography and several open-source programs proved to be a powerful combination to noninvasively image plant root samples, segment root data, and extract quantitative information (surface area and volume) from the 3D data. Our ability to visualize and measure features is always limited by scan resolution, as well as by limitations of the RooTrak software. However, scan resolution was sufficient to capture the majority of the features of the sample in this study, and RooTrak was able to successfully segment a significant portion of the root. The version of RooTrak employed in this work did not track upward traveling root segments (upward branching was not present to a significant extent in the specimen studied here); a newer version of the program addresses this problem9.
As hinted in the Introduction, a survey of the literature suggested that a costless software option to calculate root volume/surface area from 3D tomography data on specimens in the centimeter range and above was highly desirable. The significance of the approach reported here is that, after the root is segmented, it works with a 3D data format created by the widely used program ImageJ. The heart of the analysis is the calculation of the volume and surface area of the root from its image approximated by a triangular mesh. The triangular mesh generated by ImageJ in the .stl format, is represented by the indices of the triangles that make up the surface approximation. imeshJ extracts the indices from the binary file format and is able to calculate the surface area from the indices (which is simply half the magnitude of the cross product between vectors joining points [1,2] and points [1,3].) The volume is calculated by constructing a tetrahedron between the triangle points and the origin, and finding the scalar triple-product between the position vectors of the triangle. Although there are several resources for such calculations, the challenge was to efficiently compute the surface area and volume for meshes that contained upwards of 10 million triangles (as may be the case in larger root structures.) By eliminating loop structures from the code, which would process the triangles one by one, and implementing several methods that allow simultaneous processing of multiple data points, we were able to run the calculations on a 6 million-triangle mesh in under 5 sec. With regards to sample size, a potted plant in a 20-ounce container would cover the whole detector range under the same magnification and would result in a 2,000-slice image stack as dataset. If we extrapolate the speed of the calculation from a 200-slice image stack (the sample size of the processed root in this study, 6.2 mm) processed in 5 sec&#160;to a 2,000-slice image stack, imeshJ should still process that under 1 min.
The products of this method are the surface area and volume of the root sample, which were determined without removing the plant from the soil, or disturbing it in any way. The calculated surface area is 351.87 mm2, the volume is 47.27 mm3. Other, intermediate, products are a 3D visualization of the root structure, and the triangular mesh of the roots (Figure&#160;6).
The critical steps in the present protocol are the noninvasive tomographic imaging of the specimen to provide data with sufficient density contrast (step 1 in Protocol), the segmentation of the root portion of study from the rest of the sample (step 3), and the calculation of the root volume and surface area from the triangular mesh, isosurface (step 4). To achieve optimal density contrast, the X-ray power settings, 85 kV and 190 &#956;A, were chosen based on detector response for the present sample; lower X-ray power would have produced inferior color contrast, while higher power would have saturated the detector. The grey level histogram feature of the data collection software guides the user in deciding what power settings to use. In general, soil-plant samples with significant organic content tend to require lower (&#62;100 kV) X-ray voltage settings.
The accuracy of the calculated surface area is dependent on the assumption that the isosurface produced by ImageJ is a reasonable approximation to the actual surface of the root. This assumption is reasonable for large root segments, but may prove lesser so when roots with dimensions comparable to the voxel size are imaged. For the sample in this study, the size of the whole root and its segments were magnitudes greater than the voxel size. The default ImageJ/BoneJ settings (see &#34;Threshold&#34; parameter in 4.3 of Protocol) resulting in 3.6x10-4 mm2 triangles versus the 9.6x10-4 mm2 image pixel size must have provided an accurate estimate of the root surface area. For specimens with smaller features requiring greater detail, the number of triangles approximating the surface may be increased (triangle size decreased) by lowering the &#34;Threshold&#34; value. This will result in longer computational time. For root segments with dimensions approaching the voxel size, the instrumental resolution becomes the bottleneck, since the voxel representation becomes less accurate. The accuracy of the calculated volume figure is also dependent on the above assumption, but to a smaller degree because smaller-diameter roots contribute proportionally less to the volume of the root than the surface area of the root. However, a smaller cross section of the root can be scanned just as effectively if distance from the X-ray source is minimized. In other words, root segments requiring higher-resolution data, would need to be re-scanned at a higher magnification, and more accurate metrics may be obtained. The accuracy of the surface area calculation may also be affected by imperfect segmentation of the root from the soil. While our ability to distinguish the root from the soil depends on the color (grey level) contrast achieved in the imaging step, improvement of the segmentation step would help reduce any error in the calculation. Development of a new code that would improve the RooTrak segmentation process is underway. The calculation done by imeshJ was verified by comparing the output of the program for simple, single-component specimens of known volume and surface area in our laboratory.
This methodology of processing 3D data should be applicable to other material/sample systems where there is connectivity between components of similar X-ray attenuation and difficulties arise with sample segmentation. imeshJ will work on 3D data from any sources (PET, MRI scans) on any object of interest as long as an image stack is created from the data, which is used to create an isosurface, and the STL file that imeshJ uses. The calculated surface area and volume figures should be compared to values obtained on the same sample by other means (to be developed) to assess the accuracy of these calculations. Such comparison will be important for our ability to further refine the imeshJ code. Future plans include the development of a new root tracking tool and the imeshJ code for high-throughput imaging of plant root samples.

Roots, as part of the rhizosphere2-5, represent an &#34;invisible&#34; part of plant biology since soil makes it difficult to image roots non-invasively 6, 7. However, studying root growth and interaction within the soil environment is critical to understanding root/plant growth and nutrient cycling, which in turn affect forestation, food security, and climate. X-ray computed tomography (XCT) has proven to be a valuable tool for noninvasive imaging of plant root samples in their local environments 8. In order to measure root development and dimensional changes under different conditions, and be able to compare data from different datasets/specimens, one needs to extract quantitative information from the tomography data. Segmentation of the root data from that of the surrounding soil, that is, the isolation of the root image from everything else around it (including, for example, a neighboring plant) is a critical step before accurate size analysis can be done. However, a simple thresholding approach is often unfeasible for root data. The challenges associated with imaging plant roots in soil include variations in the X-ray attenuation properties of the root material, and the overlap in attenuation values between root and soil caused by water and organic matter. These issues have been superbly addressed recently by Mairhofer et al. in their visual tracking tool RooTrak 7, 9. The next step after a successful segmentation is the accurate determination of root volume and surface area. The volume may be estimated by counting the number of voxels and multiplying by the voxels&#39; size cubed as shown before7. For a more accurate determination of root surface area and volume, the isosurface of the segmented root system can be represented by a mesh of triangles, using an algorithm known as Marching Cubes 10. The open-source ImageJ11 can be employed to approximate the root volume based on the Marching Cubes algorithm. To the best of our knowledge, only a limited number of open-source software dedicated to calculating tomography-based volume/surface data for root specimens in the centimeter range and above is currently available 12. One open-source software we looked at13 focuses on root growth and is aimed at cellular features enabling quantitative volume analysis at single-cell resolution. Some open-source software dedicated to whole root systems14 is excellent for small-diameter tubular root systems based on the approximation that their shape is actually tubular. However, some work with 2D images and are unable to handle 3D stacks14. Furthermore, the tubular shape approximation may not be valid when root systems with rough surfaces and non-uniform shapes, such as those of trees, are studied. Another approach15 uses two-dimensional (2D) rotational image sequences innovatively circumventing the need for a costly CT scanner. It measures, records, and displays root system lengths. The software we have tested from those only available commercially16-18; one does not appear to be able to handle 3D image stacks16, the second is a leaf area and root length measurement tool17, while the third is based on color analysis18. Based on this survey, we suggest that a costless option that approximates the surface and volume of the root regardless of its shape from 3D tomography data is desirable.
Building on the freely available RooTrak and ImageJ, we have developed a program, named imeshJ (see Supplemental Code File) which processes an isosurface mesh (surface stereolithography file) generated from segmented root data, and calculates the volume and surface area of the root by doing simple geometrical calculations on the mesh triangle index data. Here we report a method that combines the use of XCT imaging, data reconstruction and visualization (software CT Pro 3D and VG Studio), segmentation of the root of the specimen from the soil in the 3D data (open-source software ImageJ and RooTrak), and extraction of the surface and volume information from a triangular mesh (ImageJ and the computer code imeshJ).

<table><tbody><tr><td>X-Tek/Metris XTH 320/225 kV&amp;nbsp;</td><td>Nikon Metrology</td><td></td><td>X-ray tomography scanner</td></tr><tr><td>Inspect X</td><td>Nikon Metrology</td><td></td><td>Instrument control software</td></tr><tr><td>CT Pro 3D</td><td>Nikon Metrology</td><td></td><td>Reconstruction software, version XT 2.2</td></tr><tr><td>VG Studio MAX</td><td>Visual Graphics GmbH</td><td></td><td>Visualization software for 3D volumes, version 2.1.5</td></tr><tr><td>ImageJ</td><td>Open-source</td><td></td><td>Image processing and analysis software, version 1.6</td></tr><tr><td>RooTrak</td><td>Open-source</td><td></td><td>Root segmentation software, version 0.3.1-b1 beta</td></tr><tr><td>imeshJ</td><td>EMSL</td><td></td><td>MATLAB script developed by the authors</td></tr><tr><td>Prairie dropseed grass sample</td><td></td><td></td><td>Sample obtained from ground in residential area</td></tr></tbody></table>

extracting metrics for three-dimensional root systems: volume and surface analysis from in-soil x-ray computed tomography data

Plant roots play a critical role in plant-soil-microbe interactions that occur in the rhizosphere, as well as processes with important implications to climate change and crop management. Quantitative size information on roots in their native environment is invaluable for studying root growth and environmental processes involving plants. X-ray computed tomography (XCT) has been demonstrated to be an effective tool for in situ root scanning and analysis. We aimed to develop a costless and efficient tool that approximates the surface and volume of the root regardless of its shape from three-dimensional (3D) tomography data. The root structure of a Prairie dropseed (Sporobolus heterolepis) specimen was imaged using XCT. The root was reconstructed, and the primary root structure was extracted from the data using a combination of licensed and open-source software. An isosurface polygonal mesh was then created for ease of analysis. We have developed the standalone application imeshJ, generated in MATLAB1, to calculate root volume and surface area from the mesh. The outputs of imeshJ are surface area (in mm2) and the volume (in mm3). The process, utilizing a unique combination of tools from imaging to quantitative root analysis, is described. A combination of XCT and open-source software proved to be a powerful combination to noninvasively image plant root samples, segment root data, and extract quantitative information from the 3D data. This methodology of processing 3D data should be applicable to other material/sample systems where there is connectivity between components of similar X-ray attenuation and difficulties arise with segmentation.

The specimen consisting of two stems of the native grass Prairie dropseed (Sporobolus heterolepis) and the original soil around it was taken from a residential area and placed in a small tube-shaped holder seen in Figure&#160;1. The reconstructed data voxel size was approximately 31 &#181;m x 31 &#181;m x 31 &#181;m. The reconstructed volume file was used to create a stack of images from a selected orientation (top view) using the open-source image-processing program ImageJ 1.611. The volume data was also brightened in this program to increase contrast between root and soil values. From the reconstructed data, it was clear that the root and some components of the soil, most likely organic matter, have very similar X-ray attenuation factors resulting in little to no grey-scale contrast in the images (Figure&#160;2).
RooTrak, the program used for segmentation, is an open source program developed at the Center for Integrative Plant Biology at the University of Nottingham7. It is specifically designed to track roots as they branch out through soil, and isolate the roots from surrounding material to produce a stack of binary images. RooTrak has been shown to produce segmentations better than simple thresholding for root data7, 9. A seed point was selected inside each of the pertinent root sections visible in the top slice of the volume data (Figure&#160;3) then the tracking function of the software was run. The RooTrak parameters &#34;Smoothness&#34; and &#34;Similarity&#34; were set to 0.3 and 0.8 respectively. This range constantly provides good grey value separation and isolates the region of interest well. RooTrak successfully segmented the selected 200 slices of volume data (Figure&#160;4), which was equivalent to a depth of 6.2 mm. See segmentation of the root in the animation file (Rootvideo.mov).
ImageJ was used to generate a triangular mesh, isosurface, of the 3D volume (approximating the surface of the isolated root) from the data produced by RooTrak (Figure&#160;5). Default settings employed for &#34;Resampling&#34; and &#34;Threshold&#34; in ImageJ plugin BoneJ (see 4.3 in Protocol) were selected because they produce a detailed isosurface in a relatively quick manner. Adjusting the resampling level will affect the amount of time it takes to render the isosurface. The mesh was saved in the STL format, and imeshJ was used to compute the surface area and volume of the mesh. For the reconstructed volume in the present study, the calculated surface area was 351.87 mm2, and the volume 47.27 mm3 (see Figure&#160;6).
<img alt="Figure 1" src="/files/ftp_upload/53788/53788fig1.jpg" /> 
Figure 1. The live specimen used in the study. The grass sample was imaged in its native soil in a 7&#34; tall, 1.5&#34; diameter plastic pot. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53788/53788fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/53788/53788fig2.jpg" /> 
Figure 2. The Segmentation Problem. Left: Top view of a horizontal slice of the sample showing organic matter (OM) components of similar gray level to that of the root. Right: 3D rendering of the data showing all components that are of similar grey level to the root. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53788/53788fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/53788/53788fig3.jpg" /> 
Figure 3. Starting point in RooTrak. Top left: top view slice of the volume data where seeding is started; Top right: augmented view of the slice are marked by a red square in the left figure, seed points are selected inside the pertinent root cross section; Bottom: The root saturated in red color is selected for segmentation. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53788/53788fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/53788/53788fig4.jpg" /> 
Figure 4. Comparison of slices from RooTrak. Three representative pairs of top view slices from different &#34;heights&#34; showing the root segmented by RooTrak. (A-B): top slice and the corresponding slice of the segmented stem; (C-D): mid region slice and the corresponding slice of the segmented root; (E-F): bottom region slice and the corresponding slice of the segmented root. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53788/53788fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/53788/53788fig5.jpg" /> 
Figure 5. Isosurface of the root as captured from ImageJ. The root surface was approximated by a triangular mesh. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53788/53788fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/53788/53788fig6.jpg" /> 
Figure 6. The imeshJ interface. The graphical user interface of imeshJ showing the actual results of the calculation. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53788/53788fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data at JoVE.com

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

A methodology for obtaining visual and quantitative root structure information from X-ray computed tomography data acquired in-soil is presented.

Plant roots play a critical role in plant-soil-microbe interactions that occur in the rhizosphere, as well as processes with important implications to ...

extracting-metrics-for-three-dimensional-root-systems-volume-surface

Nanyang Technological University

Research

JoVE Journal

Environment

8.5K Views.  Environmental Molecular Sciences Laboratory.  Een methodologie voor het verkrijgen van visuele en kwantitatieve wortel structuur informatie van X-ray computertomografie data verkregen in de bodem wordt gepresenteerd. 

Video: Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop cultivars against environmental stresses. An experimental protocol is presented for RGB and hyperspectral imaging of root systems. The approach uses rhizoboxes where plants grow in natural soil over a longer time to observe fully developed root systems. Experimental settings are exemplified for assessing rhizobox plants under water stress and studying the role of roots. An RGB imaging setup is described for cheap and quick quantification of root development over time. Hyperspectral imaging improves root segmentation from the soil background compared to RGB color based thresholding. The particular strength of hyperspectral imaging is the acquisition of chemometric information on the root-soil system for functional understanding. This is demonstrated with high resolution water content mapping. Spectral imaging however is more complex in image acquisition, processing and analysis compared to the RGB approach. A combination of both methods can optimize a comprehensive assessment of the root system. Application examples integrating root and aboveground traits are given for the context of plant phenotyping and plant physiological research. Further improvement of root imaging can be obtained by optimizing RGB image quality with better illumination using different light sources and by extension of image analysis methods to infer on root zone properties from spectral data.

An experimental protocol is presented for assessment of soil grown plant root systems with RGB and hyperspectral imaging. Combination of RGB image time series with chemometric information from hyperspectral scans optimizes insights into plant root dynamics.

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop ...

Measuring Stolons and Rhizomes of Turfgrasses Using a Digital Image Analysis System

Length and diameter of stolons or rhizomes are usually measured using simple rulers and calipers. This procedure is slow and laborious, so it is often used on a limited number of stolons or rhizomes. For this reason, these traits are limited in their use for morphological characterization of plants. The use of digital image analysis software technology may overcome measurement errors due to human mistakes, which tend to increase as the number and size of samples also increase. The protocol can be used for any kind of crop but is particularly suitable for forage or grasses, where plants are small and numerous. Turf samples consist of aboveground biomass and an upper soil layer to the depth of maximum rhizome development, depending on the species of interest. In studies, samples are washed from the soil, and stolons/rhizomes are cleaned by hand before analysis by digital image analysis software. The samples are further dried in a laboratory heating oven to measure dry weight; therefore, for each sample, the resultant data are total length, total dry weight, and average diameter. Scanned images can be corrected before analysis by excluding visible extraneous parts, such as remaining roots or leaves not removed with the cleaning process. Indeed, these fragments normally have much smaller diameters than stolons or rhizomes, so they can be easily excluded from analysis by fixing the minimum diameter below which objects are not considered. Stolon or rhizome density per unit area can then be calculated based on sample size. The advantage of this method is quick and efficient measurement of the length and average diameter of large sample numbers of stolons or rhizomes.

A software-based image analysis system provides an alternative method to study the morphology of stoloniferous and rhizomatous species. This protocol permits measurement of the length and diameter of stolons and rhizomes and can be applied to samples with a large amount of biomass and to a wide variety of species.

Length and diameter of stolons or rhizomes are usually measured using simple rulers and calipers. This procedure is slow and laborious, so it is often ...

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Landscape topography is a critical factor affecting soil formation and plays an important role in determining soil properties on the earth surface, as it regulates the gravity-driven soil movement induced by runoff and tillage activities. The recent application of Light Detection and Ranging (LiDAR) data holds promise for generating high spatial resolution topographic metrics that can be used to investigate soil property variability. In this study, fifteen topographic metrics derived from LiDAR data were used to investigate topographic impacts on redistribution of soil and spatial distribution of soil organic carbon (SOC). Specifically, we explored the use of topographic principal components (TPCs) for characterizing topography metrics and stepwise principal component regression (SPCR) to develop topography-based soil erosion and SOC models at site and watershed scales. Performance of SPCR models was evaluated against stepwise ordinary least square regression (SOLSR) models. Results showed that SPCR models outperformed SOLSR models in predicting soil redistribution rates and SOC density at different spatial scales. Use of TPCs removes potential collinearity between individual input variables, and dimensionality reduction by principal component analysis (PCA) diminishes the risk of overfitting the prediction models. This study proposes a new approach for modeling soil redistribution across various spatial scales. For one application, access to private lands is often limited, and the need to extrapolate findings from representative study sites to larger settings that include private lands can be important.

Landscape processes are critical components of soil formation and play important roles in determining soil properties and spatial structure in landscapes. We propose a new approach using stepwise principal component regression to predict soil redistribution and soil organic carbon across various spatial scales.

Landscape topography is a critical factor affecting soil formation and plays an important role in determining soil properties on the earth surface, as it ...

An Optimized Rhizobox Protocol to Visualize Root Growth and Responsiveness to Localized Nutrients

Roots are notoriously difficult to study. Soil is both a visual and mechanical barrier, making it difficult to track roots in situ without destructive harvest or expensive equipment. We present a customizable and affordable rhizobox method that allows the non-destructive visualization of root growth over time and is particularly well-suited to studying root plasticity in response to localized resource patches. The method was validated by assessing maize genotypic variation in plasticity responses to patches containing 15N-labeled legume residue. Methods are described to obtain representative developmental measurements over time, measure root length density in resource-containing and control patches, calculate root growth rates, and determine 15N recovery by plant roots and shoots. Advantages, caveats, and potential future applications of the method are also discussed. Although care must be taken to ensure that experimental conditions do not bias root growth data, the rhizobox protocol presented here yields reliable results if carried out with sufficient attention to detail.

Visualizing and measuring root growth in situ is extremely challenging. We present a customizable rhizobox method to track root development and proliferation over time in response to nutrient enrichment. This method is used to analyze maize genotypic differences in root plasticity in response to an organic nitrogen source.

Roots are notoriously difficult to study. Soil is both a visual and mechanical barrier, making it difficult to track roots in situ without destructive ...

Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil

We describe a method for two-dimensional (2D) visualization and quantification of the distribution of labile (i.e., reversibly adsorbed) inorganic nutrient (e.g., P, Fe, Mn) and contaminant (e.g., As, Cd, Pb) solute species in the soil adjacent to plant roots (the &#8216;rhizosphere&#8217;) at sub-millimeter (~100 &#181;m) spatial resolution. The method combines sink-based solute sampling by the diffusive gradients in thin films (DGT) technique with spatially resolved chemical analysis by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The DGT technique is based on thin hydrogels with homogeneously distributed analyte-selective binding phases. The variety of available binding phases allows for the preparation of different DGT gel types following simple gel fabrication procedures. For DGT gel deployment in the rhizosphere, plants are grown in flat, transparent growth containers (rhizotrons), which enable minimal invasive access to a soil-grown root system. After a pre-growth period, DGT gels are applied to selected regions of interest for in situ solute sampling in the rhizosphere. Afterwards, DGT gels are retrieved and prepared for subsequent chemical analysis of the bound solutes using LA-ICP-MS line-scan imaging. Application of internal normalization using 13C and external calibration using matrix-matched gel standards further allows for the quantification of the 2D solute fluxes. This method is unique in its capability to generate quantitative, sub-mm scale 2D images of multi-element solute fluxes in soil-plant environments, exceeding the achievable spatial resolution of other methods for measuring solute gradients in the rhizosphere substantially. We present the application and evaluation of the method for imaging multiple cationic and anionic solute species in the rhizosphere of terrestrial plants and highlight the possibility of combining this method with complementary solute imaging techniques.

This protocol presents a workflow for sub-mm 2D visualization of multiple labile inorganic nutrient and contaminant solute species using diffusive gradients in thin films (DGT) combined with mass spectrometry imaging. Solute sampling and high-resolution chemical analysis are described in detail for quantitative mapping of solutes in the rhizosphere of terrestrial plants.

We describe a method for two-dimensional (2D) visualization and quantification of the distribution of labile (i.e., reversibly adsorbed) inorganic ...

Leveraging Micro-CT Scanning to Analyze Parasitic Plant-Host Interactions

Micro-CT scanning has become an established tool in investigating plant structure and function. Its non-destructive nature, combined with the possibility of three-dimensional visualization and virtual sectioning, has allowed novel and increasingly detailed analysis of complex plant organs. Interactions among plants, including between parasitic plants and their hosts, can also be explored. However, sample preparation before scanning becomes crucial due to the interaction between these plants, which often differ in tissue organization and composition. Furthermore, the broad diversity of parasitic flowering plants, ranging from highly reduced vegetative bodies to trees, herbs, and shrubs, must be considered during the sampling, treatment, and preparation of parasite-host material. Here two different approaches are described for introducing contrast solutions into the parasite and/or host plants, focusing on analyzing the haustorium. This organ promotes connection and communication between the two plants. Following a simple approach, details of haustorium tissue organization can be explored three-dimensionally, as shown here for&#160;euphytoid, vine, and mistletoe parasitic species. Selecting specific contrasting agents and application approaches also allow detailed observation of endoparasite spread within the host body and detection of direct vessel-to-vessel connection between parasite and host, as shown here for an obligate root parasite. Thus, the protocol discussed here can be applied to the broad diversity of parasitic flowering plants to advance the understanding of their development, structure, and functioning.

Micro-CT is a non-destructive tool that can analyze plant structures in three dimensions. The present protocol describes the sample preparation to leverage micro-CT to analyze parasitic plant structure and function. Different species are used to highlight the advantages of this method when coupled with specific preparations.

Micro-CT scanning has become an established tool in investigating plant structure and function. Its non-destructive nature, combined with the possibility ...

High-Throughput Tree Ring Analysis and Density Estimation

Tree Core Analysis with X-ray Computed Tomography

An X-ray computed tomography (CT) toolchain is presented to obtain tree-ring width (TRW), maximum latewood density (MXD), other density parameters, and quantitative wood anatomy (QWA) data without the need for labor-intensive surface treatment or any physical sample preparation. The focus here is on increment cores and scanning procedures at resolutions ranging from 60 &#181;m down to 4 &#181;m. Three scales are defined at which wood should be looked at: (i) inter-ring scale, (ii) ring scale, i.e., tree-ring analysis and densitometry scale, as well as (iii) anatomical scale, the latter approaching the conventional thin-section quality. Custom-designed sample holders for each of these scales enable high-throughput scanning of multiple increment cores. A series of software routines were specifically developed to efficiently treat three-dimensional X-ray CT images of the tree cores for TRW and densitometry. This work briefly explains the basic principles of CT, which are needed for a proper understanding of the protocol. The protocol is presented for some known species that are commonly used in dendrochronology. The combination of rough density estimates, TRW and MXD data, as well as quantitative anatomy data, allows us to broaden and deepen current analyses for climate reconstructions or tree response, as well as further develop the field of dendroecology/climatology and archeology.

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

Here we show how to process tree cores with an X-ray computed tomography toolchain. Except for chemical extraction for some purposes, no further physical lab treatment is needed. The toolchain can be used for biomass estimations, for obtaining MXD/tree-ring width data as well as for obtaining quantitative wood anatomy data.

An X-ray computed tomography (CT) toolchain is presented to obtain tree-ring width (TRW), maximum latewood density (MXD), other density parameters, and ...

Using High Resolution Computed Tomography to Visualize the Three Dimensional Structure and Function of Plant Vasculature

High resolution x-ray computed tomography (HRCT) is a non-destructive diagnostic imaging technique with sub-micron resolution capability that is now being used to evaluate the structure and function of plant xylem network in three dimensions (3D) (e.g. Brodersen et al. 2010; 2011; 2012a,b). HRCT imaging is based on the same principles as medical CT systems, but a high intensity synchrotron x-ray source results in higher spatial resolution and decreased image acquisition time. Here, we demonstrate in detail how synchrotron-based HRCT (performed at the Advanced Light Source-LBNL Berkeley, CA, USA) in combination with Avizo software (VSG Inc., Burlington, MA, USA) is being used to explore plant xylem in excised tissue and living plants. This new imaging tool allows users to move beyond traditional static, 2D light or electron micrographs and study samples using virtual serial sections in any plane. An infinite number of slices in any orientation can be made on the same sample, a feature that is physically impossible using traditional microscopy methods.
Results demonstrate that HRCT can be applied to both herbaceous and woody plant species, and a range of plant organs (i.e. leaves, petioles, stems, trunks, roots). Figures presented here help demonstrate both a range of representative plant vascular anatomy and the type of detail extracted from HRCT datasets, including scans for coast redwood (Sequoia sempervirens), walnut (Juglans spp.), oak (Quercus spp.), and maple (Acer spp.) tree saplings to sunflowers (Helianthus annuus), grapevines (Vitis spp.), and ferns (Pteridium aquilinum and Woodwardia fimbriata). Excised and dried samples from woody species are easiest to scan and typically yield the best images. However, recent improvements (i.e. more rapid scans and sample stabilization) have made it possible to use this visualization technique on green tissues (e.g. petioles) and in living plants. On occasion some shrinkage of hydrated green plant tissues will cause images to blur and methods to avoid these issues are described. These recent advances with HRCT provide promising new insights into plant vascular function.

High resolution x-ray computed tomography (HRCT) is a non-destructive diagnostic imaging technique that can be used to study the structure and function of plant vasculature in 3D. We demonstrate how HRCT facilitates exploration of xylem networks across a wide range of plant tissues and species.

High resolution x-ray computed tomography (HRCT) is a non-destructive diagnostic imaging technique with sub-micron resolution capability that is now being ...

Biology

Using Flatbed Scanners to Collect High-resolution Time-lapsed Images of the Arabidopsis Root Gravitropic Response

Research efforts in biology increasingly require use of methodologies that enable high-volume collection of high-resolution data. A challenge laboratories can face is the development and attainment of these methods. Observation of phenotypes in a process of interest is a typical objective of research labs studying gene function and this is often achieved through image capture. A particular process that is amenable to observation using imaging approaches is the corrective growth of a seedling root that has been displaced from alignment with the gravity vector. Imaging platforms used to measure the root gravitropic response can be expensive, relatively low in throughput, and/or labor intensive. These issues have been addressed by developing a high-throughput image capture method using inexpensive, yet high-resolution, flatbed scanners. Using this method, images can be captured every few minutes at 4,800 dpi. The current setup enables collection of 216 individual responses per day. The image data collected is of ample quality for image analysis applications.

This protocol describes a process for rapid collection of images of Arabidopsis seedlings responding to a gravity stimulus using commercially-available flatbed scanners. The method allows for inexpensive, high-volume capture of high-resolution images amenable for downstream analysis algorithms.

Research efforts in biology increasingly require use of methodologies that enable high-volume collection of high-resolution data. A challenge laboratories ...

Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

It is necessary to accurately quantify the anatomical structures of maize materials based on high-throughput image analysis techniques. Here, we provide a 'sample preparation protocol' for maize materials (i.e., stem, leaf, and root) suitable for ordinary microcomputed tomography (micro-CT) scanning. Based on high-resolution CT images of maize stem, leaf, and root, we describe two protocols for the phenotypic analysis of vascular bundles: (1) based on the CT image of maize stem and leaf, we developed a specific image analysis pipeline to automatically extract 31 and 33 phenotypic traits of vascular bundles; (2) based on the CT image series of maize root, we set up an image processing scheme for the three-dimensional (3-D) segmentation of metaxylem vessels, and extracted two-dimensional (2-D) and 3-D phenotypic traits, such as volume, surface area of metaxylem vessels, etc. Compared with traditional manual measurement of vascular bundles of maize materials, the proposed protocols significantly improve the efficiency and accuracy of micron-scale phenotypic quantification.

We provide a novel method to improve the X-ray absorption contrast of maize tissue suitable for ordinary microcomputed tomography scanning. Based on CT images, we introduce a set of image-processing workflows for different maize materials to effectively extract microscopic phenotypes of vascular bundles of maize.

It is necessary to accurately quantify the anatomical structures of maize materials based on high-throughput image analysis techniques. Here, we provide a ...

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

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