This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.

8.10.9. nilearn.plotting.plot_roi

nilearn.plotting.plot_roi(roi_img, bg_img=<MNI152Template>, cut_coords=None, output_file=None, display_mode='ortho', figure=None, axes=None, title=None, annotate=True, draw_cross=True, black_bg='auto', threshold=0.5, alpha=0.7, cmap=<matplotlib.colors.LinearSegmentedColormap object>, dim='auto', vmin=None, vmax=None, resampling_interpolation='nearest', view_type='continuous', linewidths=2.5, **kwargs)[source]

Plot cuts of an ROI/mask image (by default 3 cuts: Frontal, Axial, and Lateral)

roi_imgNiimg-like object

See The ROI/mask image, it could be binary mask or an atlas or ROIs with integer values.

bg_imgNiimg-like object, optional

See The background image that the ROI/mask will be plotted on top of. If nothing is specified, the MNI152 template will be used. To turn off background image, just pass “bg_img=None”. Default=MNI152TEMPLATE.

cut_coordsNone, or a tuple of floats, optional

The MNI coordinates of the point where the cut is performed, in MNI coordinates and order. If display_mode is ‘ortho’ or ‘tiled’, this should be a 3-tuple: (x, y, z) For display_mode == ‘x’, ‘y’, or ‘z’, then these are the coordinates of each cut in the corresponding direction. If None is given, the cuts is calculated automaticaly. If display_mode is ‘mosaic’, and the number of cuts is the same for all directions, cut_coords can be specified as an integer. It can also be a length 3 tuple specifying the number of cuts for every direction if these are different.

output_filestring, or None, optional

The name of an image file to export the plot to. Valid extensions are .png, .pdf, .svg. If output_file is not None, the plot is saved to a file, and the display is closed.

display_mode{‘ortho’, ‘mosaic’, ‘tiled’, ‘x’, ‘y’, ‘z’, ‘yx’, ‘xz’, ‘yz’}, optional

Choose the direction of the cuts: ‘x’ - sagittal, ‘y’ - coronal, ‘z’ - axial, ‘ortho’ - three cuts are performed in orthogonal directions, ‘tiled’ - three cuts are performed and arranged in a 2x2 grid, ‘mosaic’ - three cuts are performed along multiple rows and columns, Default=’ortho’.

figureinteger or matplotlib figure, optional

Matplotlib figure used or its number. If None is given, a new figure is created.

axesmatplotlib axes or 4 tuple of float: (xmin, ymin, width, height), optional

The axes, or the coordinates, in matplotlib figure space, of the axes used to display the plot. If None, the complete figure is used.

titlestring, optional

The title displayed on the figure.

annotateboolean, optional

If annotate is True, positions and left/right annotation are added to the plot. Default=True.

draw_crossboolean, optional

If draw_cross is True, a cross is drawn on the plot to indicate the cut plosition.

black_bgboolean or ‘auto’, optional

If True, the background of the image is set to be black. If you wish to save figures with a black background, you will need to pass “facecolor=’k’, edgecolor=’k’” to matplotlib.pyplot.savefig. Default=’auto’.

thresholdNone, ‘auto’, or a number, optional

If None is given, the image is not thresholded. If a number is given, it is used to threshold the image: values below the threshold (in absolute value) are plotted as transparent. If auto is given, the threshold is determined magically by analysis of the image. Default=0.5.

alphafloat between 0 and 1, optional

Alpha sets the transparency of the color inside the filled contours. Default=0.7.

cmapmatplotlib colormap, optional

The colormap for the atlas maps.

dimfloat or ‘auto’, optional

Dimming factor applied to background image. By default, automatic heuristics are applied based upon the background image intensity. Accepted float values, where a typical span is between -2 and 2 (-2 = increase contrast; 2 = decrease contrast), but larger values can be used for a more pronounced effect. 0 means no dimming. Default=’auto’.

vminfloat, optional

Lower bound for plotting, passed to matplotlib.pyplot.imshow.

vmaxfloat, optional

Upper bound for plotting, passed to matplotlib.pyplot.imshow.

resampling_interpolationstr, optional

Interpolation to use when resampling the image to the destination space. Can be “continuous” to use 3rd-order spline interpolation, or “nearest” to use nearest-neighbor mapping. “nearest” is faster but can be noisier in some cases. Default=’nearest’.

view_type{‘continuous’, ‘contours’}, optional

By default view_type == ‘continuous’, rois are shown as continuous colors. If view_type == ‘contours’, maps are shown as contours. For this type, label denoted as 0 is considered as background and not shown. Default=’continuous’.

linewidthsfloat, optional

This option can be used to set the boundary thickness of the contours. Only reflects when view_type=’contours’. Default=2.5.

See also


To simply plot probabilistic atlases (4D images)


A small threshold is applied by default to eliminate numerical background noise.

For visualization, non-finite values found in passed ‘roi_img’ or ‘bg_img’ are set to zero.