Measure Neurons measures branching information for neurons or any skeleton objects with seed points.
This module measures the number of trunks and branches for each neuron in an image. The module takes a skeletonized image of the neuron plus previously identified seed objects (for instance, the neuron soma) and finds the number of axon or dendrite trunks that emerge from the soma and the number of branches along the axons and dendrites. Note that the seed objects must be both smaller than, and touching the skeleton in order to be counted.
The typical approach for this module is the following:
- Identify a seed object. This object is typically a nucleus, identified with a module such as IdentifyPrimaryObjects.
- Identify a larger object that touches or encloses this seed object. For example, the neuron cell can be grown outwards from the initial seed nuclei using IdentifySecondaryObjects.
- Use the Morph module to skeletonize the secondary objects.
- Finally, the primary objects and the skeleton objects are used as inputs to MeasureNeurons.
The module determines distances from the seed objects along the axons and dendrites and assigns branchpoints based on distance to the closest seed object when two seed objects appear to be attached to the same dendrite or axon.
Available measurements
- NumberTrunks: The number of trunks. Trunks are branchpoints that lie within the seed objects
- NumberNonTrunkBranches: The number of non-trunk branches. Branches are the branchpoints that lie outside the seed objects.
- NumberBranchEnds: The number of branch end-points, i.e, termini.
Settings:
Select the seed objects
Select the previously identified objects that you want to use as the
seeds for measuring branches and distances. Branches and trunks are assigned
per seed object. Seed objects are typically not single points/pixels but
instead are usually objects of varying sizes.
Select the skeletonized image
Select the skeletonized image of the dendrites
and/or axons as produced by the Morph module's
Skel operation.
Retain the branchpoint image?
Select Yes if you want to save the color image of
branchpoints and trunks. This is the image that is displayed
in the output window for this module.
Name the branchpoint image
(Used only if a branchpoint image is to be retained)
Enter a name for the branchpoint image here. You can then
use this image in a later module, such as SaveImages.
Maximum hole size:
(Used only when filling small holes)
This is the area of the largest hole to fill, measured
in pixels. The algorithm will fill in any hole whose area is
this size or smaller.
Export the neuron graph relationships?
Select Yes to produce an edge file and a vertex
file that give the relationships between trunks, branchpoints
and vertices.
Intensity image
Select the image to be used to calculate
the total intensity along the edges between the vertices.
Vertex file name
Enter the name of the file that will hold the edge information.
You can use metadata tags in the file name.
Each line of the file
is a row of comma-separated values. The first row is the header;
this names the file's columns. Each subsequent row represents
a vertex in the neuron skeleton graph: either a trunk,
a branchpoint or an endpoint.
The file has the following columns:
- image_number: The image number of the associated image
- vertex_number: The number of the vertex within the image
- i: The I coordinate of the vertex.
- j: The J coordinate of the vertex.
- label: The label of the seed object associated with
the vertex.
- kind: The vertex type, with the following choices:
- T: Trunk
- B: Branchpoint
- E: Endpoint
Edge file name
Enter the name of the file that will hold the edge information.
You can use metadata tags in the file name. Each line of the file
is a row of comma-separated values. The first row is the header;
this names the file's columns. Each subsequent row represents
an edge or connection between two vertices (including between
a vertex and itself for certain loops).
The file has the following columns:
- image_number: The image number of the associated image
- v1: The zero-based index into the vertex
table of the first vertex in the edge.
- v2: The zero-based index into the vertex table of the
second vertex in the edge.
- length: The number of pixels in the path connecting the
two vertices, including both vertex pixels.
- total_intensity: The sum of the intensities of the
pixels in the edge, including both vertex pixel intensities.