​The Allen Cell Structure Segmenter

Description

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

DeconvolutionLab2

Description

DeconvolutionLab2 includes a friendly user interface to run the following deconvolution 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.

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.

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Spots colocalization (ComDet)

Description

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.

HDomeTransform3D

Description

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)
hdome.runOp()
mtbdone = hdome.getResultImage()
imp2 = mtbdone.getImagePlus()
imp2.show()

Nessys

Description

Nessys: Nuclear Envelope Segmentation System

 

Nessys is a software written in Java for the automated identification of cell nuclei in biological images (3D + time). It is designed to perform well in complex samples, i.e when cells are particularly crowded and heterogeneous such as in embryos or in 3D cell cultures. Nessys is also fast and will work on large images which do not fit in memory.


Nessys also offers an interactive user interface for the curation and validation of segmentation results. Think of this as a 3D painter / editor. This editor can also be used to generate manually segmented images to use as ground truth for testing the accuracy of the automated segmentation method.


Finally Nessys, contains a utility for assessing the accuracy of the automated segmentation method. It works by comparing the result of the automated method to a manually generated ground truth. This utility will provide two types of output: a table with a number of metrics about the accuracy and an image representing a map of the mismatch between the result of the automated method and the ground truth.

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InspectJ

Description

InspectJ is a free ImageJ/FIJI tool to inspect digital image integrity.

InspectJ_v2 is a newer version for advanced users. It applies additional features like histogram equalization and gamma correction for improved image inspections.

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TTK the Topology Toolkit

Description

The Topology ToolKit (TTK) is an open-source library and software collection for topological data analysis in scientific visualization.

TTK can handle scalar data defined either on regular grids or triangulations, either in 2D or in 3D. It provides a substantial collection of generic, efficient and robust implementations of key algorithms in topological data analysis. It includes:
 · For scalar data: critical points, integral lines, persistence diagrams, persistence curves, merge trees, contour trees, Morse-Smale complexes, topological simplification;
 · For bivariate scalar data: fibers, fiber surfaces, continuous scatterplots, Jacobi sets, Reeb spaces;
 · For uncertain scalar data: mandatory critical points;
 · For time-varying scalar data: critical point tracking;
 · For high-dimensional / point cloud data: dimension reduction;
 · and more!

 

TTK makes topological data analysis accessible to end users thanks to easy-to-use plugins for the visualization front end ParaView. Thanks to ParaView, TTK supports a variety of input data formats.
 

TTK is written in C++ but comes with a variety of bindings (VTK/C++, Python) and standalone command-line programs. It is modular and easy to extend. We have specifically developed it such that you can easily write your own data analysis tools as TTK modules.

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ttk

FishInspector

Description

The software FishInspector provides automatic feature detections in images of zebrafish embryos (body size, eye size, pigmentation). It is Matlab-based and provided as a Windows executable (no matlab installation needed).

The recent version requires images of a lateral position. It is important that the position is precise since deviation may confound with feature annotations. Images from any source can be used. However, depending on the image properties parameters may have to be adjusted. Furthermore, images obtained with normal microscope and not using an automated position system with embryos in glass capillaries require conversion using a KNIME workflow (the workflow is available as well). As a result of the analysis the software provides JSON files that contain the coordinates of the features. Coordinates are provided for eye, fish contour, notochord , otoliths, yolk sac, pericard and swimbladder. Furthermore, pigment cells in the notochord area are detected. Additional features can be manually annotated. It is the aim of the software to provide the coordinates, which may then be analysed subsequently to identify and quantify changes in the morphology of zebrafish embryos.

FishInspector Logo

SpotDetectionIJ

Description

This is a classical workflow for spot detection or blob like structures (vesicules, melanosomes,...)

Step 1 Laplacian of Gaussian to enhance spots . Paraeters= radius, about the average spot radius

Step 2 Detect minima (using Find Maxima with light background option to get minima). Parameter : Tolerance to Noise: to be tested, hard to predict. About the height of the enhanced feautures peaks

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spot detection

Creating an ImageJ plugin / command

Description

The best way to start writing an ImageJ2 plugin (ImageJ2 developers call it command and not plugin) is to download the example command from github and modify it. There is a video tutorial on the whole workflow on how to do this on youtube.

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