7.6. Adding Masks

Many processing steps and markers in Junifer allow you to specify a binary mask to select voxels you want to include in the analysis. There are a number of masks in-built in Junifer already, so check if any of them suit your needs. Check how to use these masks here. Once you know how to use these masks, and you checked whether the in-built masks suit your needs, and you have found that they don’t, you can come back here to learn how to use your own masks.

The principle is fairly simple and quite similar to Adding Parcellations and Adding Coordinates. Junifer provides a register_mask() function that lets you register your own custom masks. It consists of two positional arguments (name and mask_path) and one optional keyword argument (overwrite).

The name argument is a string indicating the name of the mask. This name is used to refer to that mask in Junifer internally in order to obtain the actual mask data and perform operations on it. For example, using the name you can load a mask after registration using the load_mask() function.

The mask_path should contain the path to a valid NIfTI image with binary voxel values (i.e. 0 or 1). This data can then be used by Junifer to mask other MR images.

7.6.1. Step 1: Prepare code to register a mask

A simple script called register_custom_mask.py to register a mask could look as follows:

from junifer.data import register_custom_mask
from pathlib import Path

# this path is only an example, of course use the correct path
# on your system:
mask_path = Path("..") / ".." / "my_custom_mask.nii.gz"

register_mask(name="my_custom_mask", mask_path=mask_path)

Simple, right? Now we just have to configure a YAML file to register this mask so we can use it for codeless configuration of junifer.

7.6.2. Step 2: Configure a YAML file for registration of a mask

In order to do this, we can use the with keyword provided by Junifer:

with:
  - register_custom_mask.py

Then we can use this mask for any processing step or marker that takes in a mask as an argument. For example:

markers:
  - name: CustomMaskParcelAggregation_mean
    kind: ParcelAggregation
    parcellation: Schaefer200x17
    method: mean
    masks: "my_custom_mask"

Now, you can simply use this YAML file to run your pipeline. One important point to keep in mind is that if the paths given in register_custom_mask.py are relative paths, they will be interpreted by Junifer as relative to the jobs directory (i.e. where Junifer will create submit files, logs directory and so on). For simplicity, you may just want to use absolute paths to avoid confusion, yet using relative paths is likely a better way to make your pipeline directory/repository more portable and therefore more reproducible for others. Really, once you understand how these paths are interpreted by Junifer, it is quite easy.