The Imaris 10 release brings significant improvements to the filament tracing module. Filament tracing in Imaris 10 runs much faster, it is capable of segmenting much larger images, and it performs tracing of networks much better than before. In addition, the Imaris 10 filament tracing module uses machine learning to achieve greater versatility.

The Imaris 10 release also adds the capability to use Swiping in Circle Select Mode to select objects, which can be very useful for working with Spots, Surfaces, Cells or Filaments.

Filament New Workflow for Neuron Tracing

The workflow for tracing neurons starts with soma detection, followed by detection of seed points along the filament, from which Imaris then computes segments that the user may classify into good and bad. The user only needs to classify the subset of seed points (around 50 in each group) and this subset becomes a training set for the machine learning seed point classification. Imaris automatically classifies the remaining seed points. The process of adding seed points to the training set and having Imaris classify all other seed points can be repeated multiple times to achieve the best results. Finally seed points on good segments are traced back to the soma along shortest paths in the segment network.

The computation of segments between pairs of seed points is new in Imaris 10. This step was introduced to run multiple fast marching computations [Sethian 1999] in parallel and to give users the ability to classify segments into groups of good and bad segments before the final tracing is performed.

Filament New Workflow for Network Tracing

The workflow to trace networks in Imaris 10 is very similar to the neuron tracing workflow. It starts with the detection of seed points along the filaments from which Imaris then computes segments that the user may classify into good and bad. In the final step, the bad segments are removed from the segment network (with the exception of bad segments that close short gaps) to produce the final network.

The computation of segments between pairs of seed points together with the machine learning classification of segments facilitate this new workflow for network tracing.

Filament Multi-Scale Seed Points and Multi-Scale Fast Marching

To trace filaments with significant variations in diameter, Imaris 10 has the capability to do multi-scale seed point detection. For this the user enters the diameter of the smallest seed points and a large diameter, larger than the largest seed points. For each seed point Imaris then automatically determines the best scale according to a scale-selection model [Lindeberg 1998].

Subsequently Imaris computes segments between the largest seed points to first determine the thick filaments. Following the detection of thick filaments Imaris computes thin segments between smaller seed points and thick segments.

For this approach to be successful it is useful to ensure good quality for the large diameter seed points. You can do this by manually deleting “bad” large seed points or by training the machine learning classification for seed points.

Filament Distance to Surfaces Statistics for Regional Restrictions

Sometimes you might want to restrict filament detection to a region inside or outside a Surface. In previous versions of Imaris you would have accomplished this by “masking”. In Imaris 10 if you turn on “object-object statistics” for a surface and the filament, then the machine learning seed point classification will be trained with a shortest distance to surface statistics value for each seed point. This creates the possibility for the classifier to learn from the shortest distance to surface statistics. This provides an easy to use way to restrict the seed points to a certain distance to the surface.

Imaris 10.0.1