Region Extraction using a t-statistical map (3D)

This example shows how to extract regions or separate the regions from a statistical map.

We use localizer t-statistic maps from nilearn.datasets.fetch_neurovault_auditory_computation_task as an input image.

The idea is to threshold an image to get foreground objects using a function nilearn.image.threshold_img and extract objects using a function nilearn.regions.connected_regions.

Fetching t-statistic image of localizer contrasts by loading from datasets utilities

from nilearn import datasets

localizer = datasets.fetch_neurovault_auditory_computation_task()
tmap_filename = localizer.images[0]

Threshold the t-statistic image by importing threshold function

from nilearn.image import threshold_img

# Two types of strategies can be used from this threshold function
# Type 1: strategy used will be based on scoreatpercentile
threshold_percentile_img = threshold_img(tmap_filename, threshold='97%', copy=False)


# Type 2: threshold strategy used will be based on image intensity
# Here, threshold value should be within the limits i.e. less than max value.
threshold_value_img = threshold_img(tmap_filename, threshold=3.0, copy=False)

Visualization Showing thresholding results by importing plotting modules and its utilities

from nilearn import plotting

# Showing percentile threshold image
plotting.plot_stat_map(threshold_percentile_img, display_mode='z', cut_coords=5,
                       title='Threshold image with string percentile', colorbar=False)

# Showing intensity threshold image
plotting.plot_stat_map(threshold_value_img, display_mode='z', cut_coords=5,
                       title='Threshold image with intensity value', colorbar=False)

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<nilearn.plotting.displays._slicers.ZSlicer object at 0x7ff8f2fad570>

Extracting the regions by importing connected regions function

from nilearn.regions import connected_regions

regions_percentile_img, index = connected_regions(threshold_percentile_img,
                                                  min_region_size=1500)

regions_value_img, index = connected_regions(threshold_value_img,
                                             min_region_size=1500)

Visualizing region extraction results

title = ("ROIs using percentile thresholding. "
         "\n Each ROI in same color is an extracted region")
plotting.plot_prob_atlas(regions_percentile_img, bg_img=tmap_filename,
                         view_type='contours', display_mode='z',
                         cut_coords=5, title=title)
title = ("ROIs using image intensity thresholding. "
         "\n Each ROI in same color is an extracted region")
plotting.plot_prob_atlas(regions_value_img, bg_img=tmap_filename,
                         view_type='contours', display_mode='z',
                         cut_coords=5, title=title)
plotting.show()

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/home/alexis/miniconda3/envs/nilearn/lib/python3.10/site-packages/nilearn/plotting/displays/_axes.py:71: UserWarning:

No contour levels were found within the data range.

Estimated memory usage: 9 MB