Efficiently obtaining a reliable coronary artery centerline from computed tomography angiography
data is relevant in clinical practice. Whereas numerous methods have been presented for this
purpose, up to now no standardized evaluation methodology has been published to reliably evaluate
and compare the performance of the existing or newly developed coronary artery centerline
This evaluation framework provides a large-scale standardized evaluation methodology and reference
database for the quantitative evaluation of coronary artery centerline extraction algorithms.
Well-defined measures are presented for the evaluation of coronary artery centerline extraction algorithms, a database containing thirty-two cardiac CTA datasets with corresponding reference standard is described and made available, and different methods are available to extract statistics from the evaluation results.
26 coronary artery centerline extraction methods are evaluated with the framework.
Still open for submission
This study started with the MICCAI 2008 workshop "3D Segmentation in the Clinic: A Grand Challenge II" at the 11th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in September 2008.
After the workshop we have kept en will keep the website open for submissions. The thirty-two cardiac CTA datasets, and the corresponding
reference standard centerlines for the
training data, are available for download for anyone
who wishes to validate their algorithm. Extracted
centerlines can be submitted and the obtained results
can be used in a publication.
Details about the evaluation framework (data, reference standard, measures, scores and ranking) can be found in M. Schaap, C. Metz, et al., Standardized Evaluation Methodology and Reference Database for Evaluating Coronary Artery Centerline Extraction Algorithms, Medical Image Analysis, 2009.
We discern three different categories of coronary artery centerline extraction algorithms: automatic
extraction methods, methods with minimal user interaction and interactive extraction methods.
Category 1: automatic extraction
Automatic extraction methods find the centerlines
of coronary arteries without user interaction.
In order to evaluate the performance of automatic
coronary artery centerline extraction, two points
per vessel are provided to extract the coronary
artery of interest:
- Point A: a point inside the distal part of the
vessel; this point unambiguously defines the
vessel to be tracked;
- Point B: a point approximately 3 cm (measured
along the centerline) distal of the start
point of the centerline.
Point A should be used for selecting the appropriate
centerline. If the automatic extraction result does
not contain centerlines near point A, point B can
be used to select the appropriate centerline. Point
A and B are only meant for selecting the right centerline
and it is not allowed to use them as input
for the extraction algorithm.
Category 2: extraction with minimal user interaction
Extraction methods with minimal user interaction
are allowed to use one point per vessel as input
for the algorithm. This can be either one of the
- Point A or B, as defined above;
- Point S: the start point of the centerline;
- Point E: the end point of the centerline;
- Point U: any manually defined point.
Points A, B, S and E are provided with the data.
Furthermore, in case the method obtains a vessel
tree from the initial point, point A or B may be
used after the centerline determination to select the
Category 3: interactive extraction
All methods that require more user-interaction
than one point per vessel as input are part of category
3. Methods can use e.g. both points S and E
from category 2, a series of manually clicked positions,
or one point and a user-defined threshold.
The training data and testing data are stored in archives with directories for
each dataset. The directories uniquely describe the datasets. The training
datasets are numbered 00 to 07 and are stored in the directories dataset00
to dataset07. The testing set is stored in the directories dataset08 to
dataset31. Each directory datasetXX contains an image file, named imageXX.mhd and
imageXX.raw, and four directories for the vessels, these are named vessel0,
vessel1, vessel2, and vessel3. These directories contain the reference standard
and point A, B, S and E for each vessel.
Image data format
All image data is stored in Meta format containing an ASCII readable header
and a separate raw image data file. This format is ITK compatible. Full documentation
is available at . An application
that can read the data is SNAP (). If you want to
write your own code to read the data, note that in the header file you can find
the dimensions of each file. In the raw file the values for each voxel are stored
consecutively with index running first over x, then y, then z. The pixel type is
unsigned short. A gray value (GV) of 0 corresponds to -1024 Hounsfield units
(HU) and 1024GV corresponds to 0HU (i.e. HU(x) = GV(x) - 1024).
Reference standard files
The files named reference.txt contain the reference standard paths for each
vessel. The files contain the world position of the x-, y-, and z-coordinate of
each path point, the radius at that point (ri) and the inter-observer variability
of that position (ioi), in case of the averaged reference standard. Every point is
on a different line in the file starting with the most proximal point and ending
with the most distal point of the vessel. The voxel coordinate of each point can
be calculated by dividing the world coordinate by the voxel size of the image.
The voxel size can be found in the ElementSpacing line of the .mhd file. The
coordinates (0, 0, 0) correspond to the border of the first voxel and not to its
center. The center of the first voxel therefore has the coordinates (ElementSpacing/2,
ElementSpacing/2, ElementSpacing/2). A typical reference.txt file looks like this:
with n the number of points of the path.
The files pointA.txt, pointB.txt, pointS.txt, and pointE.txt contain respectively
the A, B, S and E point for each vessel. These files contain three values,
corresponding with the x-,y- and z-coordinate of the respective point.
A participant should create an archive (.rar, .tar.gz, .tar or .zip files are
supported) similar to the directory structure of
the training and testing data. It should contain a directory for each dataset.
These directories should be named dataset00 to dataset07if one
is submitting results on the training data and dataset00 to dataset31
if one is submitting results on the testing data. Note that testing submissions
should include all datasets (including the training data).
The directories should contain 4 subdirectories,
named vessel0 to vessel3, with a file called result.txt. This file should
contain the extracted centerline. It should contain one point per line, ordered
from proximal to distal. Each point should be described by three values corresponding
to the x-,y- and z-coordinate of each point. These points should be in
world-coordinates; similar to the input point and reference files.
An example testing submission archive: