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Ec-Clem AUTOFINDER

by Perrine Paul-Gilloteaux

Automatic registration in 2D or 3D based on detection or binary mask.

Publication Id
ICY-U1U5X2
See technical details
View complete changelog

Documentation

Please note that you should use java 8 to use this plugin (simply upgrade you java installation, nothing to be done from ICY itself). Check the Icy console message after installation to see your java version.

Also look to EC-CLEM plugin

An example of Visual Protocol using this plugin is given here

Tutorials:


Datasets and tutorial are available for some example (real data or synthetic data). Each table below presents the input data: source (likely elecron microscopy data) and target (likely light microscopy) in first raw. Second raw is the source image positionned in target, followed by target image positionned on source. Please do not reuse without permission.


  Source Target  
Input data Source Simulated Spot LM PDF Step by Step introduction and associated dataset (do not reuse without permission)
Data registered using AutoFinder Source Simulated Spot LM

Detection Parameters:

Source spot detection scale 2 (about 3 pixels); Target: manual selection;

AutoFinder Parameters:

Find small part in bigger field of view Reverse
; 1 micron; 90%

 


  Source Target  
Input data Source nuclei PDF Step by Step introduction and associated dataset (do not reuse without permission)
Data registered using AutoFinder Source Simulated Spot LM

Detection Parameters:

Source: spot detection scale 4 (about 13 pixels); Target: detection scale 3 (about 7 pixels) ;

AutoFinder Parameters:

Find small part in bigger field of view
; 10 microns; 70%

 


  Source Target  
Input data Source Simulated Spot LM PDF Step by Step  and synthetic associated dataset (do not reuse without permission)
Data registered using AutoFinder Source Simulated Spot LM

Detection Parameters:

Source and target: ConvertBinarytoRoi 0.2 microns

AutoFinder Parameters:

Find small part in bigger field of view
; 1 micron; 70%


  Source Target  
Input data Source Simulated Spot LM PDF Step by Step  and synthetic associated dataset (do not reuse without permission)
Data registered using AutoFinder Source Simulated Spot LM Detection Parameters:

Source and target: ConvertBinarytoRoi 3 microns

AutoFinder Parameters:

 About the same content, 10 microns 50%



  Source Target  
Input data Source Simulated Spot LM PDF Step by Step  and synthetic associated dataset (do not reuse without permission)
Data registered using AutoFinder Source Simulated Spot LM

Detection Parameters:

Source and Target: spot detection scale 2 (about 3 pixels);

AutoFinder Parameters:

Find small part in bigger field of view
; 1 micron; 70%



  Source Target  
Input data Source Simulated Spot LM PDF Step by Step  and synthetic associated dataset (do not reuse without permission)
Data registered using AutoFinder Source Simulated Spot LM

Parameters: convertbinarytoroipoints 0.2 microns

Autofinder: find small part in bigger field of view

: 1 microns, 90%

 

  Source Target  
Input data Source Simulated Spot LM PDF Step by Step  (also demonstrating  the use of protocols) and associated dataset (do not reuse without permission)
Data registered using AutoFinder Source Simulated Spot LM

Detection Parameters:

Target spot detection scale 3 (about 7 pixels); Source: spot detection scale 5 (about 25 pixels);

AutoFinder Parameters:

Find small part in bigger field of view
; 1 micron; 70%

 


  Source Target  
Input data Source Simulated Spot LM PDF Step by Step  and synthetic associated dataset (do not reuse without permission)
Data registered using AutoFinder Source Simulated Spot LM

Parameters:

Source and target: ConvertBinarytoRoi 2 microns

AutoFinder Parameters:

Find small part in bigger field of view
; 10 microns; 50%

You need to first have identified ROI on both images, only their center will be considered.
The plugin Spot detector is a good choice here,
just make sure to have activated Export to Roi as output;
or eC-CLEM tool ConvertBinaryToPointRoi
IMPORTANT: Check your metadata first as it will be used by MyAutoFinder.

    • About the same content in both n-D images :
      This option will try to fit the full content of the image, assuming you have similar detections
    • Find Small Part in Bigger View (reverse or not) :
      The purpose here is to find an image position (typically EM)
      on a larger field of view (typically LM). The prealignment will be different
    • Max Error in microns: :
      A pointshould have a distance to its closest matching point below this value
      in order not to be considered as an outlier. Increase if no transformation was found.
      Rule of Thumb: about 10 pixels
    • Percentage of target point to keep: :
      This is the minimum percentage of point that have to match: 90% means almost no outliers
      50% or less if the number of detection are very different. 70% is usually a good trade off


A wizard is also available

wizard