Create snapshots of segmentation maps produced by neuroimaging software. Inspired by tools like nilearn, visualqc, fmriprep and others.
nisnap c1.nii.gz c2.nii.gz c3.nii.gz --bg /tmp/raw.nii.gz --opacity 50 -o /tmp/snapshot.gif
nisnap labels.nii.gz --bg raw.nii.gz --opacity 50 --axes x --contours -o /tmp/snapshot.gifArguments:
files segmentation map(s) to create snapshots from
optional arguments:
--bg BG background image on which segmentations will be plotted.
--axes AXES choose the direction of the cuts (among 'x', 'y', or 'z')
--opacity OPACITY opacity (in %) of the segmentation maps when plotted over a background image. Only used if a background image is provided.
--contours if True, segmentations will be rendered as contoured regions. If False, will be rendered as superimposed masks.
-o OUTPUT, --output OUTPUT
snapshot will be stored in this file. If extension is .gif, snapshot will be rendered as an animation.
--config CONFIG [XNAT mode] XNAT configuration file
--nobg [XNAT mode] no background image. Plots segmentation maps only.
-e EXPERIMENT, --experiment EXPERIMENT
[XNAT mode] ID of the experiment to create snapshots from.
--resource RESOURCE [XNAT mode] name of the resource to download
--cache [XNAT mode] skip downloads (e.g. if running for a second time
--disable_warnings
--verboseExample:
import nisnap
filepaths = ['c1.nii.gz', 'c2.nii.gz', 'c3.nii.gz']
bg = 'source.nii.gz'
nisnap.plot_segment(filepaths, bg=bg, opacity=30, axes='x', animated=True)def plot_segment(filepaths, axes='xyz', bg=None, opacity=30, slices=None,
animated=False, savefig=None, contours=False, rowsize=None,
figsize=None, width=2000):
"""Plots a set of segmentation maps/masks.
Parameters
----------
filepaths: a list of str
Paths to segmentation maps (between 1 and 3). Must be of same dimensions
and in same reference space.
axes: string, or a tuple of strings
Choose the direction of the cuts (among 'x', 'y', or 'z')
bg: None or str
Path to the background image that the masks will be plotted on top of.
If nothing is specified, the segmentation maps/masks will be plotted only.
The opacity (in %) of the segmentation maps when plotted over a background
image. Only used if a background image is provided. Default: 10
slices: None, or a tuple of floats
The indexes of the slices that will be rendered. If None is given, the
slices are selected automatically.
animated: boolean, optional
If True, the snapshot will be rendered as an animated GIF.
If False, the snapshot will be rendered as a static PNG image. Default:
False
savefig: string, optional
Filepath where the resulting snapshot will be created. If None is given,
a temporary file will be created and/or the result will be displayed
inline in a Jupyter Notebook.
contours: boolean, optional
If True, segmentations will be rendered as contoured regions. If False,
will be rendered as superimposed masks. Default: False
rowsize: None, or int, or dict
Set the number of slices per row in the final compiled figure.
Default: {'x': 9, 'y': 9, 'z': 6}
figsize: None, or a 2-uple of floats, or dict
Sets the dimensions of one row of slices.
Default: {'x': (37, 3), 'y': (40, 3), 'z': (18, 3)}
width: int, optional
Width (in px) of the final compiled figure. Default: 2000.
See Also
--------
xnat.plot_segment : To plot segmentation maps directly providing their
experiment_id on an XNAT instance
"""nisnap --config .xnat.cfg -e EXPERIMENT_ID --resource ASHS --axes A --opacity 50 -o /tmp/test.gifExample:
from nisnap import xnat
xnat.plot_segment(config='/home/grg/.xnat.cfg', experiment_id='BBRC_E000',
raw=True, opacity=30, axes='x', slices=range(100,120,2), figsize=(15,5),
animated=True)def plot_segment(config, experiment_id, savefig=None, slices=None,
resource_name='SPM12_SEGMENT_T2T1_COREG',
axes='xyz', raw=True, opacity=10, animated=False, rowsize=None,
figsize=None, width=2000, contours=False, cache=False):
"""Download a given experiment/resource from an XNAT instance and create
snapshots of this resource along a selected set of slices.
Parameters
----------
config: string
Configuration file to the XNAT instance.
experiment_id : string
ID of the experiment from which to download the segmentation maps and
raw anatomical image.
savefig: string, optional
Filepath where the resulting snapshot will be created. If None is given,
a temporary file will be created and/or the result will be displayed
inline in a Jupyter Notebook.
slices: None, or a tuple of floats
The indexes of the slices that will be rendered. If None is given, the
slices are selected automatically.
resource_name: string, optional
Name of the resource where the segmentation maps are stored in the XNAT
instance. Default: SPM12_SEGMENT_T2T1_COREG
axes: string, or a tuple of strings
Choose the direction of the cuts (among 'x', 'y', 'z')
raw: boolean, optional
If True, the segmentation maps will be plotted over a background image
(e.g. anatomical T1 or T2, as in xnat.download_resources). If False,
the segmentation maps will be rendered only. Default: True
opacity: integer, optional
The opacity (in %) of the segmentation maps when plotted over a background
image. Only used if a background image is provided. Default: 10
animated: boolean, optional
If True, the snapshot will be rendered as an animated GIF.
If False, the snapshot will be rendered as a static PNG image. Default:
False
rowsize: None, or int, or dict
Set the number of slices per row in the final compiled figure.
Default: {'x': 9, 'y': 9, 'z': 6}
figsize: None, or a 2-uple of floats, or dict
Sets the dimensions of one row of slices.
Default: {'x': (37, 3), 'y': (40, 3), 'z': (18, 3)}
width: int, optional
Width (in px) of the final compiled figure. Default: 2000.
contours: boolean, optional
If True, segmentations will be rendered as contoured regions. If False,
will be rendered as superimposed masks. Default: False
cache: boolean, optional
If False, resources will be normally downloaded from XNAT. If True,
download will be skipped and data will be looked up locally.
Default: False
Notes
-----
Requires an XNAT instance where SPM segmentation maps will be found
following a certain data organization in experiment resources named
`resource_name`.
See Also
--------
xnat.download_resources : To download resources (e.g. segmentation maps +
raw images) from an XNAT instance (e.g. prior to snapshot creation)
nisnap.plot_segment : To plot segmentation maps directly providing their
filepaths
"""def download_resources(config, experiment_id, resource_name, destination,
raw=True, cache=False):
"""Download a given experiment/resource from an XNAT instance in a local
destination folder.
Parameters
----------
config: string
Configuration file to the XNAT instance.
See http://xgrg.github.io/first-steps-with-pyxnat/ for more details.
experiment_id : string
ID of the experiment from which to download the segmentation maps and
raw anatomical image.
resource_name: string
Name of the resource where the segmentation maps are stored in the XNAT
instance.
destination: string
Destination folder where to store the downloaded resources.
raw: boolean, optional
If True, a raw anatomical image will be downloaded along with the
target resources. If False, only the resources referred to by
`resource_name` will be downloaded. Default: True
cache: boolean, optional
If False, resources will be normally downloaded from XNAT. If True,
download will be skipped and data will be looked up locally.
Default: False
Notes
-----
Requires an XNAT instance where SPM segmentation maps will be found
following a certain data organization in experiment resources named
`resource_name`.
See Also
--------
xnat.plot_segment : To plot segmentation maps directly providing their
experiment_id on an XNAT instance
nisnap.plot_segment : To plot segmentation maps directly providing their
filepaths
"""pip install nisnap
Greg Operto and Jordi Huguet (BarcelonaBeta Brain Research Center)