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This pipeline was assembled over the years, so many projects and code bases have contributed to make this pipeline what it is. As such, several dependencies are required to run this pipeline. When possible, dependencies are included for easier installation. You will need to install four core components to use this pipeline. Although details are included in our installation guide, if you encounter any issues, it's best to reference the module's own repository and documentation for assistance.
The input data consists of a .RAW and its paired .DAT file. Both of these can be generated by the NorthStar Imaging (NSI) efX-CT Software by exporting a .RAW volume. By default, the volume is assumed to be in unsigned 16-bit format.
Output
The results of this pipeline are a .TSV of features and a .CSV of traits calculated from the provided volume.
Description of Traits
Below are brief descriptions of the traits reported by this pipeline. For a more detailed description of each trait, see implementation.
Glossary
Point cloud data (PCD): a collection of voxels of an object—derived from segemented volume or slices of 3D X-ray data.
Root model: PCD representation of root
Skeleton: PCD filterd using Palàyi method
Voxel: a unit of volume in three-dimensional space; equivalent two a pixel in 2D space
Traits
Name
Description
SurfaceArea
The sum of exposed voxel faces on surface of the root model.
Volume
The sum of voxels in root model; a typical proxy for "biomass" in digital phenotyping.
ConvexVolume
The volume of the convex hull that encompasses the root model.
Solidity
The volume divided by the convex hull, a measure of the thoroughness of root exploration.
MedR
The median number of roots among all horizontal slices.
MaxR
The 84th percentile value of the number of roots among all horizontal slices.
Bushiness
The ratio of the maximum to the median number of roots among all horizontal slices.
Depth
The number of voxels of the vertical axis of the root model, a measure of the depth of the deepest root
HorEqDiameter
Maximum root model width among all horizontal slices.
TotalLength
Root length as approximated by the number of voxels in the skeleton.
SRL
Specific root length; the total length divided by the volume, similar to the traditional measure of total length divided by biomass.
LengthDistr
The ratio of root length in the upper ⅓ of the root model to the root length in the lower ⅔ of the model.
WD_Ratio
Width-to-depth ratio; the maximum root model width divided by the depth.
NumberBifCl
Estimated number of branching point in the skeleton.
AvgSizeBifCl
Estimated number of branches at each branching point in the skeleton.
EdgeNum
Number of skeleton segments between estimated branching points.
AvgEdgeLength
The average length of skeleton segments between estimated branching points, a measure of branching density of the root system.
NumberTips
Number of root tips in the root model.
AvgRadius
The average radius of all roots in the model, as estimated by the distance of each voxel in the skeleton from the surface of the root model.
Elongation
PCA on 3D point cloud, taking the ratio between PC2 variance and PC1 variance; measures how elongated the root is.
Flatness
PCA on 3D point cloud, taking the ratio between PC3 variance and PC2 variance; measures how flat the root is.
Football
PCA on (x, y) of 3D point cloud, taking the ratio between PC2 variance and PC1 variance.
SolidityVHist 01-20
The solidity at each slice is computed, then spline interpolated to the nth cm (1-20) below the top.
DensityS 1-6
The frequency of voxels with different 6 overlap ratios from side view. S6 represents the largest overlap ratio. Higher numbers in greater overlap ratio means a denser root.
FractalDimensionS
Fractal dimension is estimated from the projected side-view image using the box-counting method. It is a measure of how complicated a root shape is using self-similarity.
FractalDimensionT
Fractal dimension estimated from the projected top-view image using the box-counting method. It is a measure of how complicated a root shape is using self-similarity.
N/CH/S Mean
Mean estimated from the distribution of biomass/volume (N), convex hull (CH), or solidity (S) along the z-axis.
N/CH/S Std
Standard deviation estimated from the distribution of biomass/volume (N), convex hull (CH), or solidity (S) along the z-axis.
N/CH/S Skewness
Skewness, or inequality, estimated from the distribution of biomass/volume (N), convex hull (CH), or solidity (S) along the z-axis. Negative value indicates that a large number of the values are lower than the mean (left-tailed); positive value indicates that a larger number of the values are higher than the mean (right-tailed).
N/CH/S Kurtosis
Kurtosis, or peakiness, estimated from the distribution of biomass/volume (N), convex hull (CH), or solidity (S) along the z-axis. High value indicates that the peak of the distribution around the mean is sharp and long-tailed; low value indicates that the peak around the mean is round and short-tailed.
N/CH/S Energy
Energy, or uniformity, estimated from the distribution of biomass/volume (N), convex hull (CH), or solidity (S) along the z-axis. A high value indicates that the distribution has a small number of different levels.
N/CH/S Entropy
Entropy, the inverse of energy, estimated from the distribution of biomass/volume (N), convex hull (CH), or solidity (S) along the z-axis. A high value indicates that the distribution has a higher number of different levels.
N/CH/S Smoothness
Smoothness estimated from the distribution of biomass/volume (N), convex hull (CH), or solidity (S) along the z-axis. Defined as .
This is an overview of the execution sequence for analyzing root crown x-ray scans.
Additional Information
Issues & Bug Reporting
If you encounter any error, problem, or would like to suggest a feature, please submit a git issue.
Related Projects
python-rawtools: A library for consuming and manipulating x-ray volume data in .raw format
Gia3D: A tool for measuring 3D traits from point cloud data
New3DTraitsForRPF: A tool for measuring traits using Kernel density estimation
xrt-dmt: A data management tool for tracking and archiving XRT (meta)data
References
C. Bradford Barber, David P. Dobkin, & Hannu Huhdanpaa (1996). The Quickhull algorithm for convex hulls. ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE, 22(4), 469–483.doi:10.1.1.117.405.
Fakir S. Nooruddin & Greg Turk (2003). Simplification and Repair of Polygonal Models Using Volumetric Techniques. IEEE Trans. on Visualization and Computer Graphics, vol. 9, nr. 2, April 2003, pages 191-205.
Kálmán Palágyi, & Attila Kuba (1999). Directional 3D thinning using 8 subiterations. LNCS, 325–336. doi:10.1.1.204.3009