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Pipeline operations layout

The following operations are part of the pipeline.

Input ROI

This operation allows to select the region of interest (ROI). ROI defines the dataset subarea that will be processed and analyzed by the pipeline.

Input ROI parameters

Parameter

Description

ROI

Sets the processing and analysis target space.

Current View

The selected Z plane and the viewer area are processed.

Current Plane

The selected Z plane is processed (XY).

Current Time Point

The selected time point is processed (XYZ).

Current Image Set

The complete dataset (XYZ and time) is processed.

Custom

Allows to mix the previous methods.
Note: Use the Custom option during the pipeline setting and testing. Set a sub volume (XY, Planes, Time Points, channels) of your dataset on which perform the trial. This will speed up the setting process.

Channels

Sets the processing and analysis target channels. Selecting a single channel, all the operations in the pipeline will be forced to use it.

Scaling

Sets the scaling of the dataset, which reduces it size. The measurements will not be modified by the scaling factor

Crop input data

Sets the limitation of the data used for the calculation to only the cropped selection defined above. If this option is not selected, the entire image set is used for the calculation.

Additional Parameters

Additional settings for Input ROI expand, when you click in the operation.

Parameter

Description

Bounds

Sets the analysis area edges. The whole XY bounds, the viewing area or a custom space can be applied.

Planes

Sets the analysis planes range. A single plane, a range of planes or the whole stack can be selected.

Time Points

Sets the analysis time points range. A single time pint, a range of time points or the whole movie can be selected.

Spine Tracer

The Spine Tracer operation is used to detect dendritic spines on a neuron trace. The operation is based on the trace model and requires a trace object as input.

Spine Tracer parameters

Parameter

Description

Method

Sets the method for tracing spines. Each has a set of settings which need to be properly adjusted to get the best result.

AI-assisted

This method uses a pre-trained Deep Learning Model (DeepD3) to detect spine heads and then trace them to the neurite.

In details, the deep learning model processes the intensity channel to create a probability map of the spines on which the spine heads will be detected. The intensity data is then used to connect the found spine heads to the trace by going along the neck.

Use Segments

This method uses segments created by a previous operation as spines (e.g. Blob Finder, Deep Learning Segmenter or imported in the pipeline via Import Document Object Operation).

The provided objects are not modified by this operation or connected to the trace. Instead, they are used as starting points for the spine tracing to create new approximations of the spines. The intensity data is then used to connect the provided spine heads to the trace by going along the neck..

Use Probability Map

The method is very similar to the AI-assisted method, but instead of using the built-in deep learning model to create the probability map, you must provide a pre-calculated probability map. A probability map can be created with an Image Processing operation, for example Machine Learning Probability or Deep Learning Reconstruction.

Input

 

Traces

Select an already existing neuron or neurite traces of a preceding Operation, e.g. Neuron Tracer or Import Document Objects to which the spines will be connected.

Channel/ Trace channel

Sets Intensity data where neurons and spines (necks) are visible.

Note: Trace channel is only available if Use Segments and Use Probability Map is selected.

Spine Segments

Sets segments representing the spine heads to be connected to the traces. The segments can be created with any of preceding operations or imported.

Note: Spine Segments is only available if the Use Segments method is selected.

Probability map

Sets pre-calculated image data of the spine heads (probability Map). The image should have high intensity values where the spine heads are. A probability map can be created with an Image Processing operation, for example Machine Learning Probability or Deep Learning Reconstruction.

Note: Probability map is only available if the Use Probability Map method is selected.

Parameters

 

Max. spine length

Sets the maximum distance from the spine head center to the trace along the neck. Spines that are longer are discarded.

Note: Increasing this parameter will increase the runtime since a larger area is scanned for spines.

Note: You can use the Measurements tool to determine the value.

Additional Parameters

Additional settings for the Spine Trace Segmenter expand when you click in the operation.

Parameter

Description

Head threshold

Sets Threshold to detect the spine heads on the probability map found by the deep learning model used.

Note: Higher values will result in fewer voxels being classified as spines and vice versa. This does not necessarily mean that lower values result in more spines, since two small spine heads can be merged into one by reducing the threshold, if they are close enough.

Note: Head threshold is only available if AI-assisted and Use Probability Map is selected.

Max. overlap

Sets max limit of overlap for the segments with the trace. Segments that have more overlap with the trace are discarded.

Note: Max. overlap is only available if the Use Segments method is selected.

Head size range

Sets the range by size of spine heads to be connected to the trace. Heads that do not fit into the range are discarded.

Note: Head size range is only available if AI-assisted and Use Probability Map is selected. For Use segments it is possible to use before Operations like Object Feature Filter.

Note: You can use the Measurements tool to determine the value.

Store Objects

Store the detected segments (tag) in the active dataset.

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