Angicart Example

(from example/README.md) The directory example shows an example of Angicart usage by reproducing the analysis of the paper Newberry et al. (2015)

The real substance is the
script and the comments within it. These illustrate how to process data using angicart. This page serves as a guide to using the
script to reproduce the analysis of Newberry et al. (2015) as quickly as possible and explains the context in which process runs. For further details, consult the guide page, the
output of the angicart programs themselves, the comments within the process script, or the README in the main angicart directory. The input to Angicart is a series of sequentially-names PNG images. The
directories contain the exported raster planes of original DICOM files that have been converted to PNG.


in the example directory will resample the files in dicom_N by averaging each 2x2x2-voxel cube into a single voxel as described in the Methods and Materials, Image Acquisition section of Newberry et al. (2015), and record the output in
. This step is required to run the
scripts. The
script requires ImageMagick to be installed.

process, process.large

from the Angicart source directory will execute the analysis on the resampled images (dicom_small_N) and store the results in
. Invoke the scripts from the angicart source directory as
since they refer to programs in the Angicart source directory such as
. The
script runs the analysis on the original-dimension PNG images in dicom_N, and is not recommended as the excess pixel-scale noise in these images will introduce erroneous vessels.

Reproducing the Analysis

If Angicart is installed and working correctly, the analysis should proceed as in the following transcript:
	angicart/ $ cd example
	angicart/example/ $ ./downsample_dicom.sh
	dicom_1/00000.png dicom_1/00001.png -> dicom_small_1/00000.png
	dicom_1/00002.png dicom_1/00003.png -> dicom_small_1/00001.png
	angicart/example/ $ cd ..
	angicart $ example/process
This will create the main output data in
. The output can be compared with
, the original output from Newberry et al. (2015). Note that the files may not be identical, as columns such as the color column are generated randomly. The columns of the spreadsheet are:

	tag, an arbitrary string naming the input file
	name, an arbitrary string naming the vessel segment. angicart typically uses a textual description of the vessel endpoints, as this is guaranteed to uniquely specify the vessel within a given image.
	len, the vessel segment length, in physical units (mm in the above example).
	vol, the vessel segment volume, in physical units as above.
	rad, the vessel radius, computed as the square root of (vol/(pxlen)).
	voxc, the count of voxels in the segment
	defc, the number of voxels outside a distance rad + 1 from the vessel centerline.
	col, the color of a vessel in the .vis.gd file.
	tips, the number of vessel endpoints downstream of this vessel.
	parent, the name of this vessel's parent
	beta, the ratio of this vessel's radius to its parent's radius, or NA if no parent exists.
	gamma, the ratio of this vessel's length to its parent's length, or NA if no parent exists.
	nchild, the number of children of this vessel.
	q, the conserved exponent of radius, if any, of the downstream branching junction.
	s, the conserved exponent of length, if any, of the downstream branching junction.
which can be imported into an editor for viewing or further analysis, as shown below.

Output files to verify your analysis are available to download as a zipped directory: dicom_small_out.zip.

Input images Skeletonization Segmentation