3D multi-modal co-registration


While researching @bogovicj suggestion in my other thread, I noticed a poster on the Paraview forum made a pretty good ‘ND Viewer for Microscopy’ wish list, see here.

In particular I also have a need for 3D multi-modal co-registration. Has anybody used Paraview and/or ITK toolkits for this. How does the solution compare to other solutions like BigWarp + SciView.

Are there other tools I should look at for 3D registration?


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Hi @bnorthan

Besides bigwarp + sciview, I’m aware of:

As far as I can tell, 3d slicer doesn’t do non-linear manual registration. That is, all non-linear registration is automatic - quite a limitation. I’m almost certain that is possible with ec-clem.

Something may possible with paraview, but it’s not meant for this kind of thing, and therefore I expect it would be quite hack. Wouldn’t recommend using it for alignment, unless I’m totally wrong about its capabilities. (That a kitware person “didn’t know what you were talking about” is a good sign its not meant for this purpose )

There are others, probably, and when I think of them, I’ll post here.



Hey Brian @bnorthan

I’ve worked on this for a couple of years using plastimatch. It is a tool based on ITK with tons of convenience functions and advanced tools on top. Especially its interoperability with other platforms is outstanding. For example it reads file formats from the medical imaging field ( such as broken medival DICOM files) no other software can read. As it’s command line based it nicely integrates into other software.


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Hi all,

the tool we use in ZEISS for this is called ZEN Connect. It allows to align and register multi-modal datasets in 2D/3D and is tightly integrated with the 3dXL viewer. Using the 3rd party import in ZEN (using BioFormats) one can load (more or less) any image into it.

I know it is a commercial tool, but you still might want to check it out :slight_smile:

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We developed an ImageJ/Fiji plugin, which could be used for this purpose:

There’s also a newer ImageJ/Fiji plugin called Fijiyama, though I’ve not tried it yet:

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