.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/06_manipulating_images/plot_extract_regions_labels_image.py" .. LINE NUMBERS ARE GIVEN BELOW. .. 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` .. GENERATED FROM PYTHON SOURCE LINES 22-24 The original Yeo atlas ----------------------- .. GENERATED FROM PYTHON SOURCE LINES 24-37 .. 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-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_regions_labels_image_001.png :alt: plot extract regions labels image :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_regions_labels_image_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 38-43 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. .. GENERATED FROM PYTHON SOURCE LINES 45-50 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 .. GENERATED FROM PYTHON SOURCE LINES 50-53 .. code-block:: default from nilearn.regions import connected_label_regions region_labels = connected_label_regions(atlas_yeo) .. GENERATED FROM PYTHON SOURCE LINES 54-55 Plotting the new regions .. GENERATED FROM PYTHON SOURCE LINES 55-58 .. code-block:: default plotting.plot_roi(region_labels, title='Relabeled Yeo atlas', cut_coords=(8, -4, 9), colorbar=True, cmap='Paired') .. image-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_regions_labels_image_002.png :alt: plot extract regions labels image :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_regions_labels_image_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 59-72 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. .. GENERATED FROM PYTHON SOURCE LINES 72-81 .. 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')) .. GENERATED FROM PYTHON SOURCE LINES 82-87 Different connectivity modes ----------------------------- Using the parameter connect_diag=False we separate in addition two regions that are connected only along the diagonal. .. GENERATED FROM PYTHON SOURCE LINES 87-96 .. 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-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_regions_labels_image_003.png :alt: plot extract regions labels image :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_regions_labels_image_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 97-101 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 .. GENERATED FROM PYTHON SOURCE LINES 103-109 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. .. GENERATED FROM PYTHON SOURCE LINES 109-116 .. 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-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_regions_labels_image_004.png :alt: plot extract regions labels image :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_regions_labels_image_004.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 35.580 seconds) **Estimated memory usage:** 686 MB .. _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 :width: 150 px .. 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 :download:`Download Jupyter notebook: plot_extract_regions_labels_image.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_