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analyze_training.py
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analyze_training.py
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import numpy as np
import nibabel as nib
from dipy.reconst.dti import fractional_anisotropy
from dipy.reconst.dti import TensorModel
from dipy.reconst.gqi import GeneralizedQSamplingModel
from dipy.reconst.dsi import DiffusionSpectrumDeconvModel
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel
from dipy.reconst.csdeconv import ConstrainedSDTModel
from dipy.data import get_sphere
from dipy.viz.mayavi.spheres import show_odfs
from dipy.reconst.shm import sf_to_sh
from dipy.reconst.shm import real_sph_harm_mrtrix
from dipy.core.geometry import cart2sphere
from dipy.reconst.csdeconv import odf_sh_to_sharp
from total_variation import tv_denoise_4d
from dipy.core.ndindex import ndindex
from dipy.reconst.odf import peak_directions
from show_streamlines import show_streamlines
from conn_mat import connectivity_matrix
from dipy.io.pickles import save_pickle, load_pickle
from load_data import get_specific_data, get_train_mask
from reconst_to_conmat import pipe, streams_to_connmat
from time import time
training_yn = [True, False]
categories = ['dti', 'hardi', 'dsi']
snrs = [10, 20, 30]
odf_deconvs = [True, False]
total_variations = [True, False]
denoised_data = [0, 1, 2, 3] # not denoised, coupe, rician, gaussian
dname = 'data/'
dres = 'results/'
def create_file_prefix(training, category, snr, denoised, odf_deconv, tv, method):
if training:
filename = 'train'
else:
filename = 'test'
filename += '_' + str(category) + '_snr_' + str(snr) + '_denoised_'
filename += str(int(denoised)) + '_odeconv_' + str(int(odf_deconv))
filename += '_tv_' + str(int(tv)) + '__' + method
return filename
def peaks_extract(out_file, odf, affine, sphere,
relative_peak_threshold=.5,
peak_normalize=1,
min_separation_angle=45,
max_peak_number=5):
num_peak_coeffs = max_peak_number * 3
peaks = np.zeros(odf.shape[:-1] + (num_peak_coeffs,))
for index in ndindex(odf.shape[:-1]):
vox_peaks, values, _ = peak_directions(odf[index], sphere,
float(relative_peak_threshold),
float(min_separation_angle))
if peak_normalize == 1:
values /= values[0]
vox_peaks = vox_peaks * values[:, None]
vox_peaks = vox_peaks.ravel()
m = vox_peaks.shape[0]
if m > num_peak_coeffs:
m = num_peak_coeffs
peaks[index][:m] = vox_peaks[:m]
peaks_img = nib.Nifti1Image(peaks.astype(np.float32), affine)
nib.save(peaks_img, out_file)
def save_odfs_peaks(training, odf, affine, sphere, dres, prefix):
nib.save(nib.Nifti1Image(odf, affine), dres + prefix + 'odf.nii.gz')
peaks_extract(dres + prefix + 'peaks.nii.gz',
odf, affine, sphere,
relative_peak_threshold=.3,
peak_normalize=1,
min_separation_angle=25,
max_peak_number=5)
odf_sh = sf_to_sh(odf, sphere, sh_order=8, basis_type='mrtrix')
nib.save(nib.Nifti1Image(odf_sh, affine), dres + prefix + 'odf_sh.nii.gz')
if training == True:
return training_check(dres, prefix)
def training_check(dres, prefix):
seeds_per_vox = 5
num_of_cpus = 6
cmd = 'python ~/Devel/scilpy/scripts/stream_local.py -odf ' + dres + prefix + 'odf_sh.nii.gz -m data/training-data_mask.nii.gz -s data/training-data_rois.nii.gz -n -' + str(
seeds_per_vox) + ' -process ' + str(num_of_cpus) + ' -o ' + dres + prefix + 'streams.trk'
pipe(cmd)
mat, conn_mats, diffs, ratio = streams_to_connmat(dres + prefix + 'streams.trk', seeds_per_vox)
save_pickle(dres + prefix + 'conn_mats.pkl', {'mat': mat, 'conn_mats': conn_mats, 'diffs': diffs, 'ratio':ratio})
print dres + prefix + 'conn_mats.pkl'
return diffs
def prepare(training, category, snr, denoised, odeconv, tv, method):
data, affine, gtab = get_specific_data(training,
category,
snr,
denoised)
prefix = create_file_prefix(training,
category,
snr,
denoised,
odeconv,
tv,
method)
if training:
mask = nib.load('wm_mask_hardi_01.nii.gz').get_data()
else:
#mask = np.ones(data.shape[:-1])
mask = nib.load('test_hardi_30_den=1_fa_0025_dilate2_mask.nii.gz').get_data()
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
mask[FA <= 0.1] = 0
mask[FA > 1.] = 0
indices = np.where(FA > 0.7)
lambdas = tenfit.evals[indices][:, :2]
S0s = data[indices][:, 0]
S0 = np.mean(S0s)
if S0 == 0:
print 'S0 equals to 0 switching to 1'
S0 = 1
l01 = np.mean(lambdas, axis=0)
evals = np.array([l01[0], l01[1], l01[1]])
print evals, S0
return data, affine, gtab, mask, evals, S0, prefix
def csd(training, category, snr, denoised, odeconv, tv, method, weight=0.1):
data, affine, gtab, mask, evals, S0, prefix = prepare(training,
category,
snr,
denoised,
odeconv,
tv,
method)
if category == 'dti':
csd_model = ConstrainedSphericalDeconvModel(gtab, (evals, S0), sh_order=6)
if category == 'hardi':
csd_model = ConstrainedSphericalDeconvModel(gtab, (evals, S0), sh_order=8)
csd_fit = csd_model.fit(data, mask)
sphere = get_sphere('symmetric724')
odf = csd_fit.odf(sphere)
if tv == True:
odf = tv_denoise_4d(odf, weight=0.1)
save_odfs_peaks(training, odf, affine, sphere, dres, prefix)
def sdt(training, category, snr, denoised, odeconv, tv, method, weight=0.1):
data, affine, gtab, mask, evals, S0, prefix = prepare(training,
category,
snr,
denoised,
odeconv,
tv,
method)
if category == 'dti':
csdt_model = ConstrainedSDTModel(gtab, ratio = evals[1] / evals[0], sh_order=6)
if category == 'hardi':
csdt_model = ConstrainedSDTModel(gtab, ratio = evals[1] / evals[0], sh_order=8)
csdt_fit = csdt_model.fit(data, mask)
sphere = get_sphere('symmetric724')
odf = csdt_fit.odf(sphere)
if tv == True:
odf = tv_denoise_4d(odf, weight=weight)
save_odfs_peaks(training, odf, affine, sphere, dres, prefix)
def gqi(training, category, snr, denoised, odeconv, tv, method, weight=0.1, sl=3.):
data, affine, gtab, mask, evals, S0, prefix = prepare(training,
category,
snr,
denoised,
odeconv,
tv,
method)
model = GeneralizedQSamplingModel(gtab,
method='gqi2',
sampling_length=sl,
normalize_peaks=False)
fit = model.fit(data, mask)
sphere = get_sphere('symmetric724')
odf = fit.odf(sphere)
if odeconv == True:
odf_sh = sf_to_sh(odf, sphere, sh_order=8,
basis_type='mrtrix')
# # nib.save(nib.Nifti1Image(odf_sh, affine), model_tag + 'odf_sh.nii.gz')
reg_sphere = get_sphere('symmetric724')
fodf_sh = odf_sh_to_sharp(odf_sh,
reg_sphere, basis='mrtrix', ratio=3.8 / 16.6,
sh_order=8, Lambda=1., tau=1.)
# # nib.save(nib.Nifti1Image(odf_sh, affine), model_tag + 'fodf_sh.nii.gz')
fodf_sh[np.isnan(fodf_sh)]=0
r, theta, phi = cart2sphere(sphere.x, sphere.y, sphere.z)
B_regul, m, n = real_sph_harm_mrtrix(8, theta[:, None], phi[:, None])
fodf = np.dot(fodf_sh, B_regul.T)
odf = fodf
if tv == True:
odf = tv_denoise_4d(odf, weight=weight)
save_odfs_peaks(training, odf, affine, sphere, dres, prefix)
def dsid(training, category, snr, denoised, odeconv, tv, method, weight=0.1):
data, affine, gtab, mask, evals, S0, prefix = prepare(training,
category,
snr,
denoised,
odeconv,
tv,
method)
model = DiffusionSpectrumDeconvModel(gtab)
fit = model.fit(data, mask)
sphere = get_sphere('symmetric724')
odf = fit.odf(sphere)
if odeconv == True:
odf_sh = sf_to_sh(odf, sphere, sh_order=8,
basis_type='mrtrix')
# # nib.save(nib.Nifti1Image(odf_sh, affine), model_tag + 'odf_sh.nii.gz')
reg_sphere = get_sphere('symmetric724')
fodf_sh = odf_sh_to_sharp(odf_sh,
reg_sphere, basis='mrtrix', ratio=3.8 / 16.6,
sh_order=8, Lambda=1., tau=1.)
# # nib.save(nib.Nifti1Image(odf_sh, affine), model_tag + 'fodf_sh.nii.gz')
fodf_sh[np.isnan(fodf_sh)]=0
r, theta, phi = cart2sphere(sphere.x, sphere.y, sphere.z)
B_regul, m, n = real_sph_harm_mrtrix(8, theta[:, None], phi[:, None])
fodf = np.dot(fodf_sh, B_regul.T)
odf = fodf
if tv == True:
odf = tv_denoise_4d(odf, weight=weight)
save_odfs_peaks(training, odf, affine, sphere, dres, prefix)
def show_odf_sample(filename):
odf = nib.load(filename).get_data()
sphere = get_sphere('symmetric724')
from dipy.viz import fvtk
r = fvtk.ren()
# fvtk.add(r, fvtk.sphere_funcs(odf[:, :, 25], sphere))
fvtk.add(r, fvtk.sphere_funcs(odf[25 - 10:25 + 10, 25 - 10:25 + 10, 25], sphere))
fvtk.show(r)
def show_conn_mats(filename):
d = load_pickle(filename)
return d['mat'], d['conn_mats'], d['diffs']
if __name__ == '__main__':
t0 = time()
# diffs = csd(training=True, category='dti', snr=10, denoised=0, odeconv=False, tv=False, method='csd_')
# diffs = csd(training=True, category='dti', snr=10, denoised=2, odeconv=False, tv=False, method='csd_')
# diffs = csd(training=False, category='dti', snr=10, denoised=0, odeconv=False, tv=False, method='csd_')
# diffs = csd(training=False, category='dti', snr=30, denoised=0, odeconv=False, tv=False, method='csd_')
# diffs = csd(training=True, category='hardi', snr=30, denoised=0, odeconv=False, tv=False, method='csd_')
# diffs = sdt(training=True, category='dti', snr=30, denoised=0, odeconv=False, tv=False, method='sdt_')
# diffs = sdt(training=True, category='hardi', snr=10, denoised=0, odeconv=False, tv=False, method='sdt_')
# diffs = gqi(training=True, category='dsi', snr=30, denoised=0, odeconv=True, tv=False, method='gqid_')
# diffs = gqi(training=True, category='dsi', snr=10, denoised=2, odeconv=True, tv=False, method='gqid_')
# diffs = gqi(training=True, category='dsi', snr=30, denoised=2, odeconv=True, tv=False, method='gqid_')
# diffs = gqi(training=True, category='dsi', snr=10, denoised=1, odeconv=True, tv=False, method='qqid_')
# diffs = dsid(training=True, category='dsi', snr=30, denoised=0, odeconv=False, tv=False, method='dsid_')
# diffs = dsid(training=True, category='dsi', snr=10, denoised=2, odeconv=False, tv=False, method='dsid_')
# diffs = dsid(training=True, category='dsi', snr=30, denoised=2, odeconv=False, tv=False, method='dsid_')
# diffs = dsid(training=True, category='dsi', snr=30, denoised=0, odeconv=True, tv=False, method='dsidd_')
# diffs = dsid(training=True, category='dsi', snr=30, denoised=2, odeconv=True, tv=False, method='dsidd_')
# diffs = dsid(training=True, category='dsi', snr=10, denoised=1, odeconv=False, tv=False, method='dsid_')
# diffs = gqi(training=True, category='dsi', snr=10, denoised=1, odeconv=True, tv=True, method='gqid_weight_0.1', weight=0.1)
# diffs = gqi(training=True, category='dsi', snr=10, denoised=1, odeconv=True, tv=True, method='gqid_weight_0.2', weight=0.2)
# diffs = gqi(training=True, category='dsi', snr=10, denoised=1, odeconv=True, tv=True, method='gqid_weight_0.3', weight=0.3)
# diffs = gqi(training=True, category='dsi', snr=10, denoised=1, odeconv=True, tv=True, method='gqid_weight_0.4', weight=0.4)
# diffs = gqi(training=True, category='dsi', snr=10, denoised=1, odeconv=True, tv=True, method='gqid_weight_0.5', weight=0.5)
# diffs = gqi(training=True, category='dsi', snr=10, denoised=1, odeconv=True, tv=True, method='gqid_weight_0.6', weight=0.6)
"""
diffs = gqi(training=True, category='dsi', snr=30, denoised=0, odeconv=True, tv=False, method='gqid_2.6_', sl=2.6)
diffs = gqi(training=True, category='dsi', snr=30, denoised=0, odeconv=True, tv=False, method='gqid_2.8_', sl=2.8)
diffs = gqi(training=True, category='dsi', snr=30, denoised=0, odeconv=True, tv=False, method='gqid_3.0_', sl=3.0)
diffs = gqi(training=True, category='dsi', snr=30, denoised=0, odeconv=True, tv=False, method='gqid_3.2_', sl=3.2)
diffs = gqi(training=True, category='dsi', snr=30, denoised=0, odeconv=True, tv=False, method='gqid_3.4_', sl=3.4)
diffs = gqi(training=True, category='dsi', snr=30, denoised=0, odeconv=True, tv=False, method='gqid_3.6_', sl=3.6)
diffs = gqi(training=True, category='dsi', snr=30, denoised=0, odeconv=True, tv=False, method='gqid_4.0_', sl=4.0)
diffs = gqi(training=True, category='dsi', snr=30, denoised=0, odeconv=True, tv=False, method='gqid_5.0_', sl=5.0)
"""
#diffs = gqi(training=True, category='dsi', snr=30, denoised=0, odeconv=False, tv=False, method='gqi_4.0_', sl=4.0)
"""
diffs = sdt(training=False, category='dti', snr=10, denoised=1, odeconv=False, tv=False, method='sdt_')
diffs = sdt(training=False, category='dti', snr=20, denoised=1, odeconv=False, tv=False, method='sdt_')
diffs = sdt(training=False, category='dti', snr=30, denoised=0, odeconv=False, tv=False, method='sdt_')
diffs = sdt(training=False, category='hardi', snr=10, denoised=1, odeconv=False, tv=False, method='sdt_')
diffs = sdt(training=False, category='hardi', snr=20, denoised=1, odeconv=False, tv=False, method='sdt_')
diffs = sdt(training=False, category='hardi', snr=30, denoised=0, odeconv=False, tv=False, method='sdt_')
diffs = csd(training=False, category='dti', snr=10, denoised=1, odeconv=False, tv=False, method='csd_')
diffs = csd(training=False, category='dti', snr=20, denoised=1, odeconv=False, tv=False, method='csd_')
diffs = csd(training=False, category='dti', snr=30, denoised=0, odeconv=False, tv=False, method='csd_')
diffs = csd(training=False, category='hardi', snr=10, denoised=1, odeconv=False, tv=False, method='csd_')
diffs = csd(training=False, category='hardi', snr=20, denoised=1, odeconv=False, tv=False, method='csd_')
diffs = csd(training=False, category='hardi', snr=30, denoised=0, odeconv=False, tv=False, method='csd_')
"""
diffs = dsid(training=False, category='dsi', snr=10, denoised=2, odeconv=False, tv=False, method='dsid_')
diffs = dsid(training=False, category='dsi', snr=20, denoised=2, odeconv=False, tv=False, method='dsid_')
diffs = dsid(training=False, category='dsi', snr=30, denoised=0, odeconv=False, tv=False, method='dsid_')
diffs = gqi(training=False, category='dsi', snr=10, denoised=2, odeconv=True, tv=False, method='gqid_')
diffs = gqi(training=False, category='dsi', snr=20, denoised=2, odeconv=True, tv=False, method='gqid_')
diffs = gqi(training=False, category='dsi', snr=30, denoised=0, odeconv=True, tv=False, method='gqid_')
#diffs = gqi(training=False, category='dsi', snr=30, denoised=0, odeconv=False, tv=False, method='gqi_4.0_', sl=4.0)
"""
diffs = gqi(training=False, category='dsi', snr=10, denoised=2, odeconv=True, tv=False, method='gqid_4.0_', sl=4.0)
diffs = gqi(training=False, category='dsi', snr=20, denoised=2, odeconv=True, tv=False, method='gqid_4.0_', sl=4.0)
diffs = gqi(training=False, category='dsi', snr=30, denoised=0, odeconv=True, tv=False, method='gqid_4.0_', sl=4.0)
"""
print time() - t0