unravel.voxel_stats.apply_mask module#
Use vstats_apply_mask from UNRAVEL to zeros out voxels in image based on a mask and direction args.
Usage:#
vstats_apply_mask -i input_image.nii.gz -mas mask.nii.gz [-dil 0] [–mean] [-tmas brain_mask.nii.gz] [-omas other_mask.nii.gz] [-di less | greater] [-o output_image.nii.gz] [-md parameters/metadata.txt] [–reg_res 50] [-mi] [-d list of paths] [-p sample??] [-v]
Usage to zero out voxels in image where mask > 0 (e.g., to exclude voxels representing artifacts):#
vstats_apply_mask -mas 6e10_seg_ilastik_2/sample??_6e10_seg_ilastik_2.nii.gz -i 6e10_rb20 -o 6e10_rb20_wo_artifacts -di greater
Usage to zero out voxels in image where mask < 1 (e.g., to preserve signal from segmented microglia clusters):#
vstats_apply_mask -mas iba1_seg_ilastik_2/sample??_iba1_seg_ilastik_2.nii.gz -i iba1_rb20 -o iba1_rb20_clusters
Usage to replace voxels in image with the mean intensity in the brain where mask > 0:#
vstats_apply_mask -mas FOS_seg_ilastik/FOS_seg_ilastik_2.nii.gz -i FOS -o FOS_wo_halo.zarr -di greater -m
- unravel.voxel_stats.apply_mask.load_mask(mask_path)[source]#
Load .nii.gz and return to an ndarray with a binary dtype
- unravel.voxel_stats.apply_mask.mean_intensity_in_brain(img, tissue_mask)[source]#
Z-score the image using the mask.
- Parameters:
img (-) – the ndarray to be z-scored.
mask (-) – the brain mask ndarray
- unravel.voxel_stats.apply_mask.dilate_mask(mask, iterations)[source]#
Dilate the given mask (ndarray) by a specified number of iterations.
- unravel.voxel_stats.apply_mask.scale_bool_to_full_res(ndarray, full_res_dims)[source]#
Scale ndarray to match x, y, z dimensions provided. Uses nearest-neighbor interpolation by default to preserve a binary data type.