This plugin implements various algorithms to segment 3D images.
Several algorithms for segmentation are proposed :
The plugin will basically test all thresholds and detect objects for all thresholds, it will then try to build a lineage of the objects detected, linking them from one threshold to the next threshold, taking possible splits into account. So different objects can be segmented with different thresholds, the plugin allows various criteria to pick the best threshold :
The other parameters are related to the minimal and maximal volumes of the objects. The thresholds tested can be tuned with 3 options with the value parameter :
For 8-bits images it is recommended to use the method Step with value between 1 and 5. For 16-bits images try Step with values between 5 and 100 depending on the dynamic of your data. Note than the more threshold tested the more memory used. In order not to test low thresholds you can specify to start with the mean value of the image as the lowest threshold. The image can be filtered before thresholding with a 3D median filter with radii proportional to the minimal volume.
Note that this plugin is inspired by the following paper : A generic classification-based method for segmentation of nuclei in 3D images of early embryos
The plugin works with two images, one containing the seeds of the objects, that can be obtained from local maxima (see 3D filters), the other image containing signal data. The program computes a local threshold around each seeds and cluster voxels with values higher than the local threshold computed. A plugin 3D maxima Finder is also available to compute the seeds.
Three methods are available for computing the value of the local threshold and 3 methods for clustering are also proposed. The option watershed can be chosen to avoid merging of close spots.
A tutorial is also avalaible : 3D Spot Segmentation Manual.
The plugin works with two images, one containing the seeds of the objects, that can be obtained from local maxima (see 3D filters), the other image containing signal data. A first threshold1 is used for seeds (only seeds with value > threshold1 will be used). A second threshold is used to cluster voxels with values > threshold2. In this implementation voxels are clustered to seeds in descending order of voxel values. An alternative implementation is available in Fiji classic watershed.
Two plugins 3D splitting and 3D Voronoi are also available, more details in this brief tutorial.
For details go to the 3D ImageJ Suite.
When using the 3D Segmentation plugins for publication, please cite :
J. Ollion, J. Cochennec, F. Loll, C. Escudé, T. Boudier. (2013) TANGO: A Generic Tool for High-throughput 3D Image Analysis for Studying Nuclear Organization. Bioinformatics 2013 Jul 15;29(14):1840-1. doi
GPL distribution (see licence ). Sources for plugins are available freely. Sources for core are available on request.