It is an interactive front-end visualization for registration software based on Elasix (VTK/ITK)

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SODA suite


Ensemble of blocks that implement SODA method for confocal and super-resolution microscopy, in 2 and 3 dimensions

Icy SODA logo



There are many methods in bio-imaging that can be parametrized. This gives more flexibility
to the user as long as tools provide easy support for tuning parameters. On the other hand, the
datasets of interest constantly grow which creates the need to process them in bulk. Again,
this requires proper tool support, if biologist is going to be able to organize such bulk
processing in an ad-hoc manner without the help of a programmer. Finally, new image
analysis algorithms are being constantly created and updated. Yet, lots of work is necessary to
extend a prototype implementation into product for the users. Therefore, there is a growing
need for software with a graphical user interface (GUI) that makes the process of image
analysis easier to perform and at the same time allows for high throughput analysis of raw
data using batch processing and novel algorithms. Main program in this area are written in
Java, but Python grow in bioinformatics and will be nice to allow easy wrap algorithm written
in this language.
Here we present PartSeg, a comprehensive software package implementing several image
processing algorithms that can be used for analysis of microscopic 3D images. Its user
interface has been crafted to speed up workflow of processing datasets in bulk and to allow
for easy modification of algorithm’s parameters. In PartSeg we also include the first public
implementation of Multi-scale Opening algorithm descibed in [1]. PartSeg allows for
segmentation in 3D based on finding connected components. The segmentation results can be
corrected manually to adjust for high noise in the data. Then, it is possible to calculate some
standard statistics like volume, mass, diameter and their user-defined combinations for the
results of the segmentation. Finally, it is possible to superimpose segmented structures using
weighted PCA method. Conclusions: PartSeg is a comprehensive and flexible software
dedicated to help biologists in processing, segmentation, visualization and the analysis of the
large microscopic 3D image data. PartSeg provides well established algorithms in an easy-touse,
intuitive, user-friendly toolbox without sacrificing their power and flexibility.


Examples include Chromosome territory analysis.


​The Allen Cell Structure Segmenter


The Allen Cell Structure Segmenter is a Python-based open source toolkit developed at the Allen Institute for Cell Science for 3D segmentation of intracellular structures in fluorescence microscope images.

It consists of two complementary elements:

  1. Classic image segmentation workflows for 20 distinct intracellular structure localization patterns. A visual “lookup table” is outlining the modular algorithmic steps for each segmentation workflow. This provides an intuitive guide for selection or construction of new segmentation workflows for a user’s particular segmentation task. 
  2. Human-in-the-loop iterative deep learning segmentation workflow trained on ground truth manually curated data from the images segmented with the segmentation workflow. Importantly, this module was not released yet.


The Allen Cell Structure Segmenter Overview



The successor of DeconvolutionLab

DeconvolutionLab2 is freely accessible and open-source for 3D deconvolution microscopy; it can be linked to well-known imaging software platforms, ImageJ, Fiji, ICY, Matlab, and it runs as a stand-alone application.

The backbone of our software architecture is a library that contains the number-crunching elements of the deconvolution task. It includes the tool for a complete validation pipeline. Inquisitive minds inclined to peruse the code will find it fosters the understanding of deconvolution.

At this stage, DeconvolutionLab2 includes a friendly user interface to run the following algortihms: Regularized Inverse Filter, Tikhonov Inverse Filter, Naive Inverse Filter, Richardson-Lucy, Richardson-Lucy Total Variation, Landweber (Linear Least Squares), Non-negative Least Squares, Bounded-Variable Least Squares, Van Cittert, Tikhonov-Miller, Iterative Constraint Tikhonov-Miller, FISTA, ISTA.

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GaussFit OnSpot



GaussFit_OnSpot is an ImageJ plugin for fitting Gaussian profiles onto selected positions in diffraction-limited images (e.g. single molecules, protein clusters, vesicles, or stars).

The plugin performs a function fit in regions of interest (ROI) around spots marked by point selections in grayscale images. Single or multiple spots can be either selected manually with the Multi-point tool or automatically with the Find Maxima function.

There is a PDF with more information, and also an example image.

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Spots colocalization (ComDet)


Quote " finding and/or analyzing colocalization of bright intensity spots (cells, particles, vesicles, comets, dots, etc) in images with heterogeneous background (microscopy, astronomy, engineering, etc). "

Uses Gaussian-Mexican hat convolution for preprocessing.



"PTA2 is an ImageJ1.x plugins that enable automatic particle tracking"

This plugin is developed specifically for single-molecule imaging, so it's good at tracking spots with noisy background. 

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"The Microscope Image Analysis Toolbox MiToBo is an extension for the widely used image processing application ImageJ and its new release ImageJ 2.0.
MiToBo ships with a set of operators ready to be used as plugins in ImageJ. They focus on the analysis of biomedical images acquired by various types of microscopes."



h-Dome transformation, useful for spot detection.

Jython code example:

from de.unihalle.informatik.MiToBo.core.datatypes.images import MTBImage
from de.unihalle.informatik.MiToBo.morphology import HDomeTransform3D
from ij import IJ

imp = IJ.getImage()
mtb = MTBImage.createMTBImage( imp.duplicate() )
hdome = HDomeTransform3D(mtb, 10.0)
mtbdone = hdome.getResultImage()
imp2 = mtbdone.getImagePlus()