Extract ROI voxels using nilearn's NiftiMasker

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Hi all, I’m completely new to nilearn and can’t get nilearn’s NiftiMasker class to work properly according to my needs. I want to extract ROI voxels from a 4D niimg object, where the 4th dimension represents different subjects. I also have one single niimg-object which serves as a template for the mask. I already set up a binary niimg mask object telling which voxels should be ‘cut out’ from each subjects 3D matrix and which can be ‘ignored’. I wrote the following code which solves the problem but is only a temporary solution since I actually wanted to use nilearn’s NiftiMasker (I want to stick as much as possible to nilearn’s build-in functions and want to avoid self-made solutions whenever possible). So this is my working code:

def cut(nifti_imgs,nifti_mask):
    
    # get mask labels
    nifti_mask_data = nifti_mask.get_data()
    
    # set output variable
    # FIXME: It would be better to already set up an 'empty' 4D nifti image object
    #        https://nipy.org/nibabel/reference/nibabel.nifti1.html#nibabel.nifti1.Nifti1Image
    roi_nifti_imgs = list()
    
    # iterate through each subject's 3D array and apply mask
    # FIXME: Is  image.iter_img() the fasted method when iterating over 4D nifti file?
    for img in image.iter_img(nifti_imgs):
        img_data = img.get_data()
        roi_nifti_data = np.where(nifti_mask_data == 1,img_data,0)
        roi_nifti_img = image.new_img_like(img,roi_nifti_data)
        roi_nifti_imgs.append(roi_nifti_img)
    
    # concatente list of 3D objects to one 4D object
    roi_nifti_imgs = image.concat_imgs(roi_nifti_imgs)
    
    return roi_nifti_imgs

I read the article Computing a Region of Interest (ROI) mask manually from nilearn’s User Guide and thought maybe I could use NiftiLabelsMasker like in the article only that in my case I only have a binary iimg mask (instead of ROI labels 1,2,3,…) and the 4th dimension does not represent a time series but different subjects. So I wrote the following code, which doesn’t work but returns a n_subjects * 1 array:

def masker_cut(nifti_imgs,nifti_mask):
    
    # create NiftiLabelsMasker instance and call .fit() method
    # FIXME: Use only needed arguments (masker's job is ONLY to cut out,
    # not to transform data!)
    masker = NiftiLabelsMasker(nifti_mask)
    masker.fit()
    
    roi_nifti_imgs = masker.transform(nifti_imgs)
    
    return roi_nifti_imgs

Did I just make a coding mistake here or am I using the NiftiLabelsMasker class wrong? Is there any other Nifti*Masker class which serves my needs? Any help is much appreciated!

2

have you tried NiftiMasker?

3

I would suggest to play with this example. http://nilearn.github.io/auto_examples/04_manipulating_images/plot_nifti_simple.html#sphx-glr-auto-examples-04-manipulating-images-plot-nifti-simple-py

NiftiMasker has an argument called mask_img.

Initialize the object

masker = NiftiMasker(mask_img=nifti_mask)

Use for loop to extract values from ROI on each subject.
signals = masker.fit_transform(img)

HTH

4

This will not work since the NiftiMasker instance asks for a 4D-array. So the use of a for-loop is not necessary in this case.

5

Thank you all for your quick answers. jeromedocks provided the right solution. NiftLabelsMasker was simply not the right solution for my case since it doesn’t ‘cut out’ voxels but returns a 2-dimensional array (number of scans, number of elements), where each element represents one ‘signal’ calculated from a 3D-array. It’s still not clear for me how this signal is computed (just a simple average of all voxels?) but this is another question which is not related to my post. NiftiMasker does the right job.