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Deformable 2D and 3D big data image registration and transformation with BigWarp

I2K 2024 From Images to Knowledge
Milan, Italy
Oct 24, 2024 9:45 AM

BigWarp is an intuitive tool for non-linear manual image registration that can scale to terabyte-sized image data. In this workshop, participants will learn to perform image registration with BigWarp, apply transformations to large images, import and export transformations to and from other tools, and fine-tune the results of automatic registration algorithms. BigWarp makes heavy use of the N5-API to store and load large image and transformation data and meta-data using the current NGFF formats HDF5, Zarr, and N5. In addition to basic usage, the concepts, tips, and best practices discussed will extend to other registration tools and help users achieve practical success for realistic and challenging data. Users will also get an introduction into an excellent use case for OME-NGFF.

Prerequisites

About this sample data

Kidney tissue from a wild-type, postnatal day 7 mouse, strain: C57BL/6J from Jackson Lab

Details can be found on open organelle

  • An xray of the sample was imaged (xray/xray-2)
  • The sample was trimmed and imaged again (xray/xray-3)
  • The sample was trimmed for a final time and imaged again (xray/xray-1)
  • The sample was imaged using FIB-SEM (em/fibsem-uint8)

All data are stored using OME-Zarr.

Running BigWarp

  • Modern: Plugins > BigDavaViewer > Big Warp Command
    • This is what we'll use in this workshop
  • Legacy: Plugins > BigDavaViewer > Big Warp

Outline

Masked transformations

  • From the clemSampleData, open em.tif and PALM_532nm_lo.tif images with Fiji.
  • Run BigWarp using EM as the fixed image and the PALM as the moving image.
  • Notice a mis-aligned mitochondrion near the "top" of the image
  • Click some landmarks around that mito only
  • Notice that the rest of the image is now mis-aligned
  • Turn on "masked" transformations
  • Press M to edit the mask

Export a displacement field

  • File > Export transformation
  • select Moving (warped) as reference
  • Specify a Root folder
    • Recommended: Make a folder called tforms.zarr in the same folder as the clem sample data.
  • Specify a new Root folder
    • Recommended: Make a folder called tforms_small.zarr in the same folder as the clem sample data.
  • Open the result in Fiji with Plugins > Transform > Read Displacement Field
    • What do you notice?

Let's do it again but with a different field of view:

  • File > Export transformation
  • select Specified as reference
  • Specify a Root folder
    • Recommended: Make a folder called tforms_new.zarr in the same folder as the clem sample data.
  • Use these settings for min and size
min(nm)     size(nm)
  29000   	  8000
   7000	      7000
   2500	      2500
  • Open the result in Fiji with Plugins > Transform > Read Displacement Field
    • What do you notice?