Visualization of ROI Surface Rendered with Streamlines¶
Here is a simple tutorial following the probabilistic CSA Tracking Example in which we generate a dataset of streamlines from a corpus callosum ROI, and then display them with the seed ROI rendered in 3D with 50% transparency.
from dipy.data import read_stanford_labels from dipy.reconst.shm import CsaOdfModel from dipy.data import default_sphere from dipy.direction import peaks_from_model from dipy.tracking.local import ThresholdTissueClassifier from dipy.tracking import utils from dipy.tracking.local import LocalTracking from dipy.tracking.streamline import Streamlines from fury import actor, window from fury.colormap import line_colors
First, we need to generate some streamlines. For a more complete description of these steps, please refer to the CSA Probabilistic Tracking Tutorial.
hardi_img, gtab, labels_img = read_stanford_labels() data = hardi_img.get_data() labels = labels_img.get_data() affine = hardi_img.affine white_matter = (labels == 1) | (labels == 2) csa_model = CsaOdfModel(gtab, sh_order=6) csa_peaks = peaks_from_model(csa_model, data, default_sphere, relative_peak_threshold=.8, min_separation_angle=45, mask=white_matter) classifier = ThresholdTissueClassifier(csa_peaks.gfa, .25) seed_mask = labels == 2 seeds = utils.seeds_from_mask(seed_mask, density=[1, 1, 1], affine=affine) # Initialization of LocalTracking. The computation happens in the next step. streamlines = LocalTracking(csa_peaks, classifier, seeds, affine, step_size=2) # Compute streamlines and store as a list. streamlines = Streamlines(streamlines)
Dataset is already in place. If you want to fetch it again please first remove the folder /home/travis/.dipy/stanford_hardi Dataset is already in place. If you want to fetch it again please first remove the folder /home/travis/.dipy/stanford_hardi Dataset is already in place. If you want to fetch it again please first remove the folder /home/travis/.dipy/stanford_hardi
We will create a streamline actor from the streamlines.
Next, we create a surface actor from the corpus callosum seed ROI. We provide the ROI data, the affine, the color in [R,G,B], and the opacity as a decimal between zero and one. Here, we set the color as blue/green with 50% opacity.
surface_opacity = 0.5 surface_color = [0, 1, 1] seedroi_actor = actor.contour_from_roi(seed_mask, affine, surface_color, surface_opacity)
Next, we initialize a ‘’Scene’’ object and add both actors to the rendering.
scene = window.Scene() scene.add(streamlines_actor) scene.add(seedroi_actor)
If you uncomment the following line, the rendering will pop up in an interactive window.
Total running time of the script: ( 0 minutes 17.461 seconds)