doc: i2k 2024 instructions

BigWarpI2K2024.md

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+# Deformable 2D and 3D big data image registration and transformation with BigWarp 
+
+I2K 2024 From Images to Knowledge<br>
+Milan, Italy<br>
+Oct 24, 2024 9:45 AM<br>
+
+* [Workshop page](https://events.humantechnopole.it/event/1/contributions/42/)
+
+
+> 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
+
+* [Download Future Fiji for your platform](https://downloads.imagej.net/fiji/future/)
+* If necessary download [bigwarp 9.3.0](https://maven.scijava.org/service/local/repositories/releases/content/sc/fiji/bigwarp_fiji/9.3.0/bigwarp_fiji-9.3.0.jar)
+* Download the sample data sets
+    * [xray data (medium 1.4G)](https://figshare.com/s/408097a1651139088651)
+    * [xray data (small 80M)](https://figshare.com/s/f7b2bdb6492678114fe4)
+    * [CLEM samples](https://figshare.com/s/442b7686fe02a0839ef7)
+    * [bigwarp supplementary files](https://figshare.com/s/4d95d3cf9ed9be123c1a)
+
+### 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](https://openorganelle.janelia.org/datasets/jrc_mus-kidney-3)
+
+* 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](https://ngff.openmicroscopy.org/0.4/).
+
+
+## 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?
+