TissueClassifierHMRF
Hello everyone,
I am presenting Bundle Analytics (BUAN), a computational framework for investigating the shapes and profiles of brain pathways across populations paper recently published in Nature Scientific Reports on 18th-Nov-2020 at 12:00 pm EST. There will be a demo of the BUAN framework in DIPY too. You can attend the DIPY meeting (dipy/dipy#2229) for the talk.
Read the paper here: https://www.nature.com/articles/s41598-020-74054-4
Thank You,
Bramsh
Hello,
Thank you all for joining the DIPY open meeting for the BUAN talk.
In case you missed it, here's the link to the recorded BUAN presentation: https://youtu.be/9Ay1rhRTaKU
Best,
Bramsh
from dipy.viz import window, actor
scene = window.Scene()
scene.SetBackground(1, 1, 1)
colors = [np.random.rand(3) for streamline in bundle1]
stream_actor = actor.line(bundle1, colors, linewidth=0.5)
scene.add(stream_actor)
window.show(scene)
`
from dipy.viz import window, actor
import numpy as np
from dipy.io.streamline import load_trk, save_trk
load your .trk file
bundle1 = load_trk("yourfile.trk", reference="yourfile.trk", bbox_valid_check=False).streamlines
or load data from dipy for this example
from dipy.data import two_cingulum_bundles
bundle1, _ = two_cingulum_bundles()
visualize the data
scene = window.Scene()
scene.SetBackground(1, 1, 1)
create random colores for sreamlines (one color for each streamline)
colors = [np.random.rand(3) for streamline in bundle1]
stream_actor = actor.line(bundle1, colors, linewidth=0.5)
scene.add(stream_actor)
Visualize it
window.show(scene)
`
`# Hitogram plot for the length of streamlines in a bundle/tractogram
import matplotlib.pyplot as plt
from dipy.tracking.streamline import length
lengths = [length(s) for s in bundle1] #Euclidean length of streamlines
lengths = [len(s) for s in bundle1] #this is for number of points per streamline
plt.hist(lengths, bins='auto')
plt.xlabel('Length')
plt.ylabel('count')
plt.show()`
`# Hitogram plot for the length of streamlines in a bundle/tractogram
import matplotlib.pyplot as plt
from dipy.tracking.streamline import length
lengths = [length(s) for s in bundle1] #Euclidean length of streamlines
plt.hist(lengths, bins='auto')
plt.xlabel('Length')
plt.ylabel('count')
plt.show()`
# You can give color to streamlines based on their lengths
scene = window.Scene()
scene.SetBackground(1, 1, 1)
#create random colors for streamlines
colors = [np.random.rand(3) for sl in range(int(max(lengths)))]
all_colors = []
for i in range(len(bundle1)):
all_colors.append(colors[int(lengths[i])-1]) #assign color to streamlines based on their length
#streamlines with same color have same length
stream_actor = actor.line(bundle1, all_colors, linewidth=1)
scene.add(stream_actor)
#Visualize it
window.show(scene)
By the way just wanted to say how much I appreciate your work and of all contributors. This is actually the best documented library with great tutorials and referencing of papers I've ever saw. This really helps developing and broaden one's horizon. Thank you for that.
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