Frequently Asked Questions

Q. What is SWC format?
A. A file in SWC format contains information representing a digitally reconstructed neuron. SWC is non-proprietary and stores the minimum amount of parameters required to represent a vector-based three-dimensional reconstruction. Files may begin with headers above the data values, each beginning with #. Parameters are organized into 7 columns, where each row within the column represents one trace point. From left to right these columns are: unique identity value for trace point, structure type, x coordinate, y coordinate, z coordinate, radius, identity value for parent (i.e. trace point that comes before and connects to the current trace point). The first 10 points of an example SWC file are provided below:

#Example header text here

1 2 4882 1797 19 9 -1

2 2 4882 1797 19 9 1

3 2 4875 1821 19 9 2

4 2 4852 1849 19 21 3

5 2 4842 1827 18 12 4

6 2 4835 1816 18 7 5

7 2 4827 1807 18 7 6

8 2 4814 1797 18 4 3

9 2 4803 1785 18 4 8

10 2 4785 1763 18 4 9

The bolded row represents one trace point which has been given an identity = 3, type = 2 (i.e. axon), X = 4875, Y = 1821, Z = 19, radius = 9, and trace point 2 is its parent (i.e. the trace point represented in the row directly above it).

Columns 1,2, and 7 are always integers. Columns 3,4,5, and 6 represent whatever units were used in the reconstructions process (e.g. pixels, micometers, etc.) and can have decimal points.

Column 1 (Identity #) must always increase in value by 1 whereas the column 7 (Parent Identity #) values have no such restriction but must be less than column 1 in the same row. Note that both rows 4 and 8 in the above example connect directly to row 3, meaning that row 3 must be a bifurcation point.

Row 1 has a parent = -1, which means that this row does not have a parent and is thus the root of the reconstruction.The commonly accepted values for Column 2 that are pertinent to the DIADEM datasets are: 1 = cell body; 2 = axon; and 3 = dendrite.


Q. Is there a difference in the metric for different data sets other than dimensionality?
A. There are a number of differences in the metric between datasets which are detailed on the individual dataset readme pages (except for the threshold differences). The thresholds, both distance and path length error thresholds, are different to account for the resolution in both XY and Z directions. The thresholds for spur (a small terminal branch) removal vary by dataset as well (some don't remover spurs). Also, the Neuromuscular projection dataset handles terminations in a different manner because of the rosette structures. The manual reconstructions end at the beginning of those structures, but the metric will not punish automated reconstructions that trace into the rosette structures.


Q. How can we set the matching threshold?
A. The readme for the current (post-competition) version of the metric describes how threshold parameters can be set.


Q. In first data set, "Cerebellar Climbing Fibers", the individual planes are merges of a panel of capture stacks. Distortions in neuron shape are visible at some of the boundaries. Is it possible to either fix it, or in addition, release the data as individual stacks that need to be merged?
A. Merging was not performed by hand, but with the leading software controlling the motorized stage of the microscope. Mechanical error limits in stage movement are evident at the small scale of climbing fibers though they are not large enough to impair manual tracing. The released data set corresponds to the original acquired images and was not tiled in a post-processing step. Thus, individual panel stacks are not available. Although the results may not be optimal, they are representative of the typical experimental configuration in a modern neuroanatomy lab.


Q. Do different datasets have drastically different thresholds?
A. Yes. The thresholds are as follows:

Cerebellar Climbing Fiber

XY Euclidean Distance: 37.33 pixels (1.4 microns)
Z Euclidean Distance: 4 images (1.3 microns)
XY Path Error: 0.075
Z Path Error: 0.18

Hippocampal CA3 Interneuron

XY Euclidean Distance: 11 pixels (2.4 microns)
Z Euclidean Distance: 14 images (4.67 microns)
XY Path Error: 0.08
Z Path Error: N/A

Neocortical Layer 1 Axon

XY Euclidean Distance: 4.76 pixels (1.4 microns)
Z Euclidean Distance: 5 images (5 microns)
XY Path Error: 0.07
Z Path Error: 0.18

Neuromuscular Projection Fiber

XY Euclidean Distance: 32 pixels (1.2 microns)
Z Euclidean Distance: N/A
XY Path Error: 0.04
Z Path Error: N/A

Olfactory Projection Fiber

XY Euclidean Distance: 3.94 pixels (1.3 microns)
Z Euclidean Distance: 5 images (5 microns)
XY Path Error: 0.08
Z Path Error: 0.2

Visual Cortical Layer 6 Neuron

XY Euclidean Distance: 9 pixels
Z Euclidean Distance: 6 images
XY Path Error: 0.08
Z Path Error: 0.2


Q. Is the source code for the DIADEM metric (scoring function) available?
A. Yes, it can be downloaded here.

The following are the MD5sums for the DIADEM metric source code:
If downloaded on/after May 7, 2012:
If downloaded between February 19, 2010 and May 7, 2012:
If downloaded between January 26, 2010 and February 19, 2010:
If downloaded between November 25, 2009 and January 26, 2010:
If downloaded before November 25, 2009:


Q. Why is the DIADEM metric so complex? Was the choice somewhat arbitrary?
A. The metric implements our best attempt to quantify the human judgement of what differentiates a good reconstruction from a bad one. Since we agreed on a "manual" gold standard, there is some inherent arbitrariness. The basic idea is simple: the nodes of the trees should be in the right position, their topological interconnectivity should be accurate, and the path distance in reasonable range. However, there are many different cases of possible "errors" or "variations", and these are judged differently depending on the impact they have on the overall structure. Moreover, the various datasets have different characteristics (representative of experimental diversity encountered in real-lab scenarios) which are reflected in additional requirements. These qualifications account for most of the metric complexity.


Q. What program can be used to open the .rar data set files? Is there a free, downloadable program for this?
A. Please carefully read the Data Set General Readme on the website. It indicates PeaZip as one example. A possible alternative is Zipgenius. These were both free last time we checked.

A google search for ".rar" will find many other hits. As usual with freeware, read carefully during installation to make sure you uncheck any add-on programs you don't want. PeaZip didn't have any last time we checked, but just in case...


Q. Is the output of the algorithm supposed to be the "segmented" tree, i.e. a binary file in which the voxels representing the neuron have one value and everything else have a different value? Or is the "digital reconstruction" simply the 1-voxel thick centerline that can be extracted from this segmentation, which then allows for determining interbranch length, bifurcation and termination nodes etc.?
A. A digital reconstruction, the output of the algorithm, consists of series of interconnected vectors, not voxels. Although in principle each of these vectors is associated with a thickness, the DIADEM metric only considers the branching topology, path distance, and position of the nodes, thus diameter does not affect the computation of the score.


Q. Are edge-detection, image thresholding, and tree enhancing filters part of the purpose of the challenge?
A. Any methods that can help automating the production of digital reconstructions from sets of images may be relevant to the DIADEM challenge.


Q. In the manual reconstructions provided for the Olfactory Projection training data set, some of the branch points appear slightly misaligned with the underlying labeled structure. Will this affect the scoring?
A. An example of a branch point that appears slightly misaligned with the underlying structure (from the OP_2 Training Round data set) is shown in the figure below (red arrow).

branch point example

Manual reconstructions have been tested to see if these points affect scoring. Specifically, a correctly re-aligned reconstruction was compared to the original file included in one of the data sets. None of the non-terminating nodes were missed (see next FAQ for further observations on terminating nodes). We have therefore left the reconstructions as they were originally traced.


Q. In the manual reconstructions provided for the Olfactory Projection training data set, some of the termination points appear to vary in terms of distance from the underlying labeled structures. Will this affect the scoring?
A. An example of two terminations points that end at varying distances compared to the underlying structures (from the OP_2 Training Round data set) is shown in the figure below (red arrows).

termination point example

Manual reconstructions have been tested to see if variation in the positions of termination trace points affects scoring. Specifically, a correctly re-aligned reconstruction was compared to the original file included in one of the data sets. Two terminal nodes were missed, resulting in a final score of 0.989. This score is nearly perfect and well within the typical range observed between two manual reconstructions by independent experts from the same underlying image stack. Because such minor differences are unlikely to affect algorithm rankings, we left the traces as they were originally traced.


Q. Does the metric account for possible floating point error in determining whether a node is within threshold distance in the Z-direction?
A. The current version of the metric provides a small additional margin to the Z component of the distance threshold in order to ensure that no floating point error can affect scoring.


Q. In the swc file, does column 2 (type or tag of the tracing point) influence the scores, that is, should the program correctly determine whether it is an axon or a dendrite?
A. No


Q. The same branch between two bifurcations can be divided by intermediate points differently. Does this choice affect the score?
A. The metric is based on the location of the nodes (bifurcations and terminations), but the distance along the path does affect the computation of the score (as explained in the Rules of the competition). Therefore, the intermediate points should follow the image path as accurately as it is necessary to ensure that the branch path length is accurately reproduced.


Q. The DIADEM metric provides a dramatically incorrect score and/or ignores a large portion of my SWC file. What am I doing wrong?
A. Most likely you have an older release of the metric (version prior to 11/25/2009) and need to download the more recent version. An error in the previous release of the metric occurred if any line of data did not contain the precise formatting expected (e.g. tabs between data, any character other than a normal space at the end of a line). The line for node 4 in the climbing fiber CF_1.swc contained a tab at the end. This caused the line to be ignored and thus all descendant nodes could not be attached to the tree. Ultimately the metric would conclude without a clear error, but would likely return very poor scores for automated traces run against the gold standard CF_1.swc. The updated metric ignores any whitespace at the end of a data line, though any non-whitespace characters (or any incorrect formatting) causes the program to terminate with an error message detailing the file and line number of the improper data format. Tabs and spaces are now treated equally to provide greater flexibility, though other programs may have more demanding format constraints. As before, lines beginning with the "#" symbol are seen as comments and are ignored.


Q. In some data sets, there are several branches that I would have manually traced differently than the training reconstruction. How can these be considered objective gold standards to evaluate automated tracings?
A. Experimental data is sometime ambiguous, and arbitrary choices are occasionally unavoidable. Lab providers have confirmed that there is subjectivity in the more complex data sets. The scoring thresholds should account for much of the subjectivity. If you feel certain that a point should have been traced differently, it is strongly suggested that you trace it how you feel it should be traced. Getting hung up trying to develop an algorithm that works around such problems is counter-productive to the purpose of DIADEM.