/
get_ismrm_seeds.py
executable file
·194 lines (145 loc) · 5.85 KB
/
get_ismrm_seeds.py
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import os
os.environ['PYTHONHASHSEED'] = '0'
import glob
import argparse
import nibabel as nib
import numpy as np
import subprocess as sp
from hashlib import md5
from nibabel.streamlines import ArraySequence, Tractogram
from nibabel.streamlines.trk import TrkFile
from dipy.io.utils import get_reference_info
def get_seeds_from_wm(wm_path, threshold=0):
wm_file = nib.load(wm_path)
wm_img = wm_file.get_fdata()
seeds = np.argwhere(wm_img > threshold)
seeds = np.hstack([seeds, np.ones([len(seeds), 1])])
seeds = (wm_file.affine.dot(seeds.T).T)[:, :3].reshape(-1, 1, 3)
n_seeds = len(seeds)
header = TrkFile.create_empty_header()
header["voxel_to_rasmm"] = wm_file.affine
header["dimensions"] = wm_file.header["dim"][1:4]
header["voxel_sizes"] = wm_file.header["pixdim"][1:4]
header["voxel_order"] = get_reference_info(wm_file)[3]
tractogram = Tractogram(
streamlines=ArraySequence(seeds),
affine_to_rasmm=np.eye(4)
)
save_path=os.path.join(os.path.dirname(wm_path), "seeds_from_wm.trk")
print("Saving {}".format(save_path))
TrkFile(tractogram, header).save(save_path)
def get_ismrm_seeds(data_dir, source, keep, weighted, threshold, voxel):
trk_dir = os.path.join(data_dir, "bundles")
if source in ["wm", "trk"]:
anat_path = os.path.join(data_dir, "masks", "wm.nii.gz")
resized_path = os.path.join(data_dir, "masks", "wm_{}.nii.gz".format(voxel))
elif source == "brain":
anat_path = os.path.join("subjects", "ismrm_gt", "dwi_brain_mask.nii.gz")
resized_path = os.path.join("subjects", "ismrm_gt",
"dwi_brain_mask_125.nii.gz")
sp.call(["mrresize", "-voxel", "{:1.2f}".format(voxel/100),
anat_path, resized_path])
if source == "trk":
print("Running Tractconverter...")
sp.call([
"python",
"tractconverter/scripts/WalkingTractConverter.py",
"-i", trk_dir,
"-a", resized_path,
"-vtk2trk"])
print("Loading seed bundles...")
seed_bundles = []
for i, trk_path in enumerate(glob.glob(os.path.join(trk_dir, "*.trk"))):
trk_file = nib.streamlines.load(trk_path)
endpoints = []
for fiber in trk_file.tractogram.streamlines:
endpoints.append(fiber[0])
endpoints.append(fiber[-1])
seed_bundles.append(endpoints)
if i == 0:
header = trk_file.header
n_seeds = sum([len(b) for b in seed_bundles])
n_bundles = len(seed_bundles)
print("Loaded {} seeds from {} bundles.".format(n_seeds, n_bundles))
seeds = np.array([[seed] for bundle in seed_bundles for seed in bundle])
if keep < 1:
if weighted:
p = np.zeros(n_seeds)
offset=0
for b in seed_bundles:
l = len(b)
p[offset:offset+l] = 1 / (l * n_bundles)
offset += l
else:
p = np.ones(n_seeds) / n_seeds
elif source in ["brain","wm"]:
weighted = False
wm_file = nib.load(resized_path)
wm_img = wm_file.get_fdata()
seeds = np.argwhere(wm_img > threshold)
seeds = np.hstack([seeds, np.ones([len(seeds), 1])])
seeds = (wm_file.affine.dot(seeds.T).T)[:, :3].reshape(-1, 1, 3)
n_seeds = len(seeds)
if keep < 1:
p = np.ones(n_seeds) / n_seeds
header = TrkFile.create_empty_header()
header["voxel_to_rasmm"] = wm_file.affine
header["dimensions"] = wm_file.header["dim"][1:4]
header["voxel_sizes"] = wm_file.header["pixdim"][1:4]
header["voxel_order"] = get_reference_info(wm_file)[3]
if keep < 1:
keep_n = int(keep * n_seeds)
print("Subsampling from {} seeds to {} seeds".format(n_seeds, keep_n))
np.random.seed(42)
keep_idx = np.random.choice(
len(seeds),
size=keep_n,
replace=False,
p=p)
seeds = seeds[keep_idx]
tractogram = Tractogram(
streamlines=ArraySequence(seeds),
affine_to_rasmm=np.eye(4)
)
save_dir=os.path.join(data_dir, "seeds")
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_path = os.path.join(save_dir, "seeds_from_{}_{}_vox{:03d}.trk")
save_path = save_path.format(
source,
"W"+str(int(100*keep)) if weighted else "all",
voxel
)
print("Saving {}".format(save_path))
TrkFile(tractogram, header).save(save_path)
os.remove(resized_path)
for file in glob.glob(os.path.join(trk_dir, "*.trk")):
os.remove(file)
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description="Convert fiber endpoints to seeds.")
parser.add_argument("--data_dir", help="Path to scoring_data directory.",
default="scoring/scoring_data")
parser.add_argument("--source", default="wm", type=str,
choices=["wm", "trk", "brain"],
help="Source for seeds: White Matter (wm) or Tracts (trk).")
parser.add_argument("--keep", default=1.0, type=float,
help="Fraction of seeds to keep during subsampling.")
parser.add_argument("--weighted", action="store_true",
help="If provided, subsample seeds weighted by fiber bundle size. "
"Only applicable if source == trk.")
parser.add_argument("--voxel", choices=[125, 75], default=125, type=int,
help="Voxel resolution for wm seeds.")
parser.add_argument("--thresh", default=0.1, type=float,
help="Only applicable if source == wm. Threshold for White Matter "
"Mask.")
args = parser.parse_args()
assert args.keep >= 0.01
get_ismrm_seeds(
args.data_dir,
args.source,
args.keep,
args.weighted,
args.thresh,
args.voxel
)