Registering images from consecutive physical sections

Hi everyone,

I would like to ask for some input regarding the registration of images acquired on consecutive physical sections of muscle tissue. In brief, I have a high-res single field of view image from section A that I would like to map back to a low-res overview image of the consecutive section B.

Background

  • Images are consecutive muscle tissue sections of 10 um thickness
  • On both sections, the membranes have been labeled with an antibody against laminin.
  • One of the sections has additionally been treated with a protease, leading to “deformed membranes”

Analysis goals

  • Find the matching location of the single field of view imaged at high-res image on the image of the entire section acquired at low resolution (a ROI would be enough)
  • Only the location is needed, but NOT necessarily to warp/deform the images to match their shapes precisely

Sample images

Only the membrane channel of:

Challenges

The structures in the consecutive sections are not identical, but are changed in size and shape, and thus only similar. The registration needs therefore to either account for the deformations or just be “relaxed” enough.

so far I have tried

I tested various settings and models in both tools, but did not find the proper settings in either of them.

Any input you might have on settings or maybe another tool would be much appreciated!
Thank you for your help :slight_smile:

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@CellKai

Could you maybe post some sort of screenshots showing the best that you currently achieved, maybe with some arrows in them pointing out places where the registration did not work?

I think this would help getting the discussion started.

Hi @CellKai,

OpenCV offers some nice feature detection and aligment tools, too.

Best regards,
Mario

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Hi @CellKai ,

In order to :

I just tested Multiple-template matching, and get the result below. Is that what you are looking for ?

Best,

R

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heroic! :star_struck:
yes, thats exactly what I was looking for. Thank you for looking into this @romainGuiet !

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I was lucky :sweat_smile: , thanks to @LThomas et al for making the tool!

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