Detect Spines Panel (3D)

Maximum distance between the dendritic surface and the potential top of the spine head.

• Spines that extend beyond that range are cut off at that distance.
• Set below 2.5 microns for regular dendritic spines.

Minimum distance between the dendritic surface and the furthest voxel identified for the spine.

• Useful to ignore protrusions that are too 'flat' to be considered as spines or to compensate for slight tracing inaccuracies.

Adjustments are useful during individual spine detection to identify spines that might be too light or too dark based on the original image intensity.

• 100%: Original image intensity is used.
• Above 100%: Pixels appear brighter to the detector.
• Below 100%: Pixels appear darker to the detector.

Works in conjunction with Minimum Height to ignore objects that are too small to be considered spines.

• 10 voxels is the default because an object with a volume smaller than 10 voxels is considered too uncertain to be taken into account for most resolutions.
• Also applies to objects that are not attached to the dendritic surface. Useful for discarding small structures that may arise from background noise.

Check this box if:

• You placed spines manually prior to using Find All.
• You used Detect all then decided to use different settings to detect more spines while keeping the spines already detected.
• You notice that, when you click a spine to detect it, the model of a spine adjacent to it is removed. This may happen in the case of colliding spines. Keep existing spines prevents the colliding new spine from removing the existing one.

Automatically detects spines based on the default settings defined in Detection Settings.

This option enables you to restrict the automatic detection to a single branch instead of the entire image.

1. Adjust the detection settings under Detection.
2. Check the box.
3. Click near the desired branch. Spines on the desired branch are automatically detected.

Once you've clicked the detect All button or clicked individual spines to detect them, the program takes a series of steps.

Spine detection steps

1. The dendritic branch is refined and displayed with an axial smear correction.
2. The program uses image segmentation to identify all the foreground voxels that are outside of the volume occupied by the dendritic model.
3. The identified voxels are clustered based on image intensity and distance to the dendritic model (defined in the Detect Spines panel). Clustering takes into account the adjacent spines scenario and leads to the identification of spine candidates.
4. Only the spines candidates that meet the detection criteria are rendered using a surface mesh (based on the Marching Cubes algorithm). The mesh is further processed to remove surface irregularities.
Detection criteria

A spine is detected and rendered if it meets all these criteria:

1. Spine extent >= Minimum height

The spine extent is based on image data; it is the shortest distance from the furthest identified voxel to the surface of the dendrite. It does not necessarily follow the spine neck to the dendritic surface.

The minimum height can be defined in the Detect Spines panel.

2. Voxel count >= Minimum count

The voxel count is based on voxel processing and is used to evaluate spine size. It is determined by the image scaling.

The Minimum count can be defined in the Detect Spines panel.

3. Ratio spine extent/base>= 0.4

The spine extent is based on image data; it is the shortest distance from the furthest identified voxel to the surface of the dendrite.

The base is determined using the Rayburst diameters calculated for spine detection. It is an evaluation of the spread of the spine at the surface of the dendrite and it takes into account axial smear correction.

4. Spine extent<=Outer range

The outer range is the maximum distance from the dendritic surface to the potential top of the spine head. It can be defined in the Detect Spines panel.

Renderings might not reflect exactly the spine profiles generated by the spine detection process since detection and rendering use different algorithms.

5. The spine meshes are displayed using multiple colors to help discriminate between adjacent spines. The spines have not been classified.

   To change the color of individual spines, and split, merge or delete spines, display the Edit Spines panel.

This option allows you to classify additional spines without changing the current classification of spines already detected.

This is particularly useful in the refining phase of synapse detection and classification: you can add spines and set their type manually without losing the classification of the spines already detected and classified.

Spines are classified into canonical types (stubby, mushroom, thin, filopodium).

Color-coding is applied to represent spine classification. Each spine type has been assigned a color.

• To change the default classification settings, click the Settings button.
• To re-classify individual spines manually, use the Edit Spines panel.

See Detect Spines: Classification settings Panel (3D) for details.

Change the default colors assigned to each type. The change applies to all spines of the same type.

Once you've clicked the Classify All button, the program processes the detected spines.

Spine classification steps

1. Spines are classified into canonical types (stubby, mushroom, thin, or filopodium) based on the method described in Automated Three-Dimensional Detection and Shape Classification of Dendritic Spines from Fluorescence Microscopy Images (Rodriguez, Ehlenberger, Dickstein, Hof, & Wearne, 2008) and according to the Settings.
2. Color-coding is applied to represent spine classification. Each spine classification has been assigned a color using Spine preferences.

   To re-classify individual spines manually, use the Edit Spines panel (see Editing spines/Classifying spines manually above).

Use to avoid panning manually while tracing. The last point clicked is automatically re-positioned at the window center.

The program displays the corrected thickness of the tree. Axial smear is automatically calculated after each detection.

Showing axial smear correction only modifies the display of the model; it doesn't affect the data.