.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here ` to download the full example code or to run this example in your browser via Binder
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_06_manipulating_images_plot_extract_regions_labels_image.py:
Breaking an atlas of labels in separated regions
=================================================
This example shows how to use
:class:`nilearn.regions.connected_label_regions`
to assign each spatially-separated region of the atlas a unique label.
Indeed, often in a given atlas of labels, the same label (number) may
be used in different connected regions, for instance a region in each
hemisphere. If we want to operate on regions and not networks (for
instance in signal extraction), it is useful to assign a different
label to each region. We end up with a new atlas that has more labels,
but each one points to a single region.
We use the Yeo atlas as an example for labeling regions,
:func:`nilearn.datasets.fetch_atlas_yeo_2011`
The original Yeo atlas
-----------------------
.. code-block:: default
# First we fetch the Yeo atlas
from nilearn import datasets
atlas_yeo_2011 = datasets.fetch_atlas_yeo_2011()
atlas_yeo = atlas_yeo_2011.thick_7
# Let's now plot it
from nilearn import plotting
plotting.plot_roi(atlas_yeo, title='Original Yeo atlas',
cut_coords=(8, -4, 9), colorbar=True, cmap='Paired')
.. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_regions_labels_image_001.png
:alt: plot extract regions labels image
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
The original Yeo atlas has 7 labels, that is indicated in the colorbar.
The colorbar also shows the correspondence between the color and the label
Note that these 7 labels correspond actually to networks that comprise
several regions. We are going to split them up.
Relabeling the atlas into separated regions
---------------------------------------------
Now we use the connected_label_regions to break apart the networks
of the Yeo atlas into separated regions
.. code-block:: default
from nilearn.regions import connected_label_regions
region_labels = connected_label_regions(atlas_yeo)
Plotting the new regions
.. code-block:: default
plotting.plot_roi(region_labels, title='Relabeled Yeo atlas',
cut_coords=(8, -4, 9), colorbar=True, cmap='Paired')
.. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_regions_labels_image_002.png
:alt: plot extract regions labels image
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
Note that the same cluster in original and labeled atlas could have
different color, so, you cannot directly compare colors.
However, you can see that the regions in the left and right hemispheres
now have different colors. For some regions it is difficult to tell
apart visually, as the colors are too close on the colormap (eg in the
blue: regions labeled around 3).
Also, we can see that there are many more labels: the colorbar goes up
to 49. The 7 networks of the Yeo atlas are now broken up into 49
ROIs.
You can save the new atlas to a nifti file using to_filename method.
.. code-block:: default
region_labels.to_filename('relabeled_yeo_atlas.nii.gz')
# The images are saved to the current folder. It is possible to specify the
# folder for saving the results, i.e.
# import os
# region_labels.to_filename(os.path.join(folder_path,
# 'relabeled_yeo_atlas.nii.gz'))
Different connectivity modes
-----------------------------
Using the parameter connect_diag=False we separate in addition two regions
that are connected only along the diagonal.
.. code-block:: default
region_labels_not_diag = connected_label_regions(atlas_yeo,
connect_diag=False)
plotting.plot_roi(region_labels_not_diag,
title='Relabeling and connect_diag=False',
cut_coords=(8, -4, 9), colorbar=True, cmap='Paired')
.. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_regions_labels_image_003.png
:alt: plot extract regions labels image
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
A consequence of using connect_diag=False is that we can get a lot of
small regions, around 110 judging from the colorbar.
Hence we suggest use connect_diag=True
Parameter min_size
-------------------
In the above, we get around 110 regions, but many of these are very
small. We can remove them with the min_size parameter, keeping only the
regions larger than 100mm^3.
.. code-block:: default
region_labels_min_size = connected_label_regions(atlas_yeo, min_size=100,
connect_diag=False)
plotting.plot_roi(region_labels_min_size, title='Relabeling and min_size',
cut_coords=(8, -4, 9), colorbar=True, cmap='Paired')
plotting.show()
.. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_regions_labels_image_004.png
:alt: plot extract regions labels image
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 31.735 seconds)
.. _sphx_glr_download_auto_examples_06_manipulating_images_plot_extract_regions_labels_image.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: binder-badge
.. image:: images/binder_badge_logo.svg
:target: https://mybinder.org/v2/gh/nilearn/nilearn.github.io/main?filepath=examples/auto_examples/06_manipulating_images/plot_extract_regions_labels_image.ipynb
:alt: Launch binder
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.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_extract_regions_labels_image.py `
.. container:: sphx-glr-download sphx-glr-download-jupyter
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