Mercurial > repos > greg > fast_fiber_tracking
comparison fast_fiber_tracking.py @ 0:4e3d4331fa58 draft
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| author | greg |
|---|---|
| date | Tue, 07 Nov 2017 13:48:48 -0500 |
| parents | |
| children | 63aa79b5ebd6 |
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| -1:000000000000 | 0:4e3d4331fa58 |
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| 1 #!/usr/bin/env python | |
| 2 import argparse | |
| 3 import shutil | |
| 4 | |
| 5 from dipy.data import fetch_sherbrooke_3shell | |
| 6 from dipy.data import fetch_stanford_hardi | |
| 7 from dipy.data import get_sphere | |
| 8 from dipy.data import read_sherbrooke_3shell | |
| 9 from dipy.data import read_stanford_hardi | |
| 10 from dipy.direction import peaks_from_model | |
| 11 from dipy.io.image import save_nifti | |
| 12 from dipy.reconst.csdeconv import (ConstrainedSphericalDeconvModel, auto_response) | |
| 13 from dipy.reconst.dti import TensorModel | |
| 14 from dipy.segment.mask import median_otsu | |
| 15 from dipy.tracking.local import LocalTracking, ThresholdTissueClassifier | |
| 16 from dipy.tracking.streamline import Streamlines | |
| 17 from dipy.tracking.utils import random_seeds_from_mask | |
| 18 from dipy.viz import actor, window | |
| 19 | |
| 20 import numpy as np | |
| 21 | |
| 22 parser = argparse.ArgumentParser() | |
| 23 parser.add_argument('--drmi_dataset', dest='drmi_dataset', help='Input dataset') | |
| 24 parser.add_argument('--output_csd_direction_field', dest='output_csd_direction_field', help='Output csd direction field dataset') | |
| 25 parser.add_argument('--output_det_streamlines', dest='output_det_streamlines', help='Output det streamlines dataset') | |
| 26 parser.add_argument('--output_fa_map', dest='output_fa_map', help='Output fa map dataset') | |
| 27 | |
| 28 args = parser.parse_args() | |
| 29 | |
| 30 interactive = False | |
| 31 | |
| 32 # Get input data. | |
| 33 input_dir = args.drmi_dataset | |
| 34 if input_dir == 'sherbrooke_3shell': | |
| 35 fetch_sherbrooke_3shell() | |
| 36 img, gtab = read_sherbrooke_3shell() | |
| 37 elif input_dir == 'stanford_hardi': | |
| 38 fetch_stanford_hardi() | |
| 39 img, gtab = read_stanford_hardi() | |
| 40 | |
| 41 data = img.get_data() | |
| 42 maskdata, mask = median_otsu(data, 3, 1, False, vol_idx=range(10, 50), dilate=2) | |
| 43 | |
| 44 response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=0.7) | |
| 45 csd_model = ConstrainedSphericalDeconvModel(gtab, response) | |
| 46 sphere = get_sphere('symmetric724') | |
| 47 csd_peaks = peaks_from_model(model=csd_model, data=data, sphere=sphere, mask=mask, relative_peak_threshold=.5, min_separation_angle=25, parallel=True) | |
| 48 | |
| 49 tensor_model = TensorModel(gtab, fit_method='WLS') | |
| 50 tensor_fit = tensor_model.fit(data, mask) | |
| 51 fa = tensor_fit.fa | |
| 52 | |
| 53 tissue_classifier = ThresholdTissueClassifier(fa, 0.1) | |
| 54 seeds = random_seeds_from_mask(fa > 0.3, seeds_count=1) | |
| 55 | |
| 56 ren = window.Renderer() | |
| 57 ren.add(actor.peak_slicer(csd_peaks.peak_dirs, csd_peaks.peak_values, colors=None)) | |
| 58 window.record(ren, out_path='csd_direction_field.png', size=(900, 900)) | |
| 59 shutil.move('csd_direction_field.png', args.output_csd_direction_field) | |
| 60 | |
| 61 streamline_generator = LocalTracking(csd_peaks, tissue_classifier, seeds, affine=np.eye(4), step_size=0.5) | |
| 62 streamlines = Streamlines(streamline_generator) | |
| 63 | |
| 64 ren.clear() | |
| 65 ren.add(actor.line(streamlines)) | |
| 66 window.record(ren, out_path='det_streamlines.png', size=(900, 900)) | |
| 67 shutil.move('det_streamlines.png', args.output_det_streamlines) | |
| 68 | |
| 69 save_nifti('fa_map.nii', fa, img.affine) | |
| 70 shutil.move('fa_map.nii', args.output_fa_map) |