Skip to content
This repository has been archived by the owner on Jun 19, 2023. It is now read-only.

Latest commit

 

History

History
64 lines (43 loc) · 3.57 KB

tutorial.md

File metadata and controls

64 lines (43 loc) · 3.57 KB
Raw
description
Getting started with brainreg

Tutorial

To test out brainreg, we supply a small mouse brain dataset to get you started.

{% hint style="info" %} For full information on how to use brainreg, please see the User guide {% endhint %}

Instructions:

Setting up

  • Download the data from here (the dataset is ~10MB, so it should download quickly).
  • Unzip the data to a directory of your choice (doesn't matter where). You should end up with a directory called test_brain with 270 .tif images
  • Open a terminal (Linux) or your command prompt (Windows)
  • Activate your conda environment

Run brainreg

To run brainreg, you need to know:

  • Where your data is (in this case, it's the path to the test_brain directory)
  • Where you want to save the output data (we'll just save it into a directory called brainreg_outputin the same directory as the test_brain)
  • The pixel sizes of your data in microns (see Image definition for details). In this case, our data is 40um per pixel in in the coronal plane and the spacing of the planes is 50um.
  • The orientation of your data. The software needs to know how you acquired your data (coronal, saggital etc.). For this cellfinder uses bg-space. Full details on how to enter your data orientation can be found here, but for this tutorial, the orientation is psl, which means that the data origin is the most posterior, superior, left voxel. For more details see Image definition
  • Which atlas you want to use (you can see which are available by running brainglobe list. In this case, we want to use a mouse atlas (as that's what our data is), and we'll use the 50um version of the Allen Mouse Brain Atlas.

{% hint style="warning" %} Normally the 50um mouse atlases are only used for testing (the registration will work much quicker at lower resolution). In this case, the test input data is very low resolution, so using a higher resolution atlas doesn't make much sense.

When using your own data, you'll probably (but not definitely) find that higher resolution atlases work better. Make sure to test out the different resolutions to see what works best {% endhint %}

To run brainreg, we put this all together in a single command:

brainreg test_brain brainreg_output -v 50 40 40  --orientation psl --atlas allen_mouse_50um

Lots of stuff will get printed to the console as brainreg runs, and when it's done (it should only take a minute or so), you will see something like:

INFO - MainProcess cli.py:230 - Finished. Total time taken: 0:00:29.15

This means that the registration is complete.

Visualising the results

brainreg comes with a plugin for napari (see Visualisation) for easy visualisation of the results.

To view your data, run napari from the same terminal/command as you ran brainreg (or open a new one and activate your conda environment). You can then drag and drop the brainreg_output directory into napari, and see the results.

{% hint style="info" %} The results are likely not perfect because (for speed and simplicity) we:

  • Used very low resolution data
  • Used a low resolution atlas
  • Left all the parameters as default (which were optimised for higher resolution atlases) {% endhint %}