def convert_all():
# Convert training data
for idx in range(1, 1 + 4):
for modality in ['T1', 'T2', 'seg']:
fname = get_neobrain('train', idx, modality)
fname_noext = fname[:-4]
convert_neobrain_to_nifti(fname_noext)
# Convert testing data
for idx in range(1, 1 + 3):
for prefix in ['i1', 'i2', 'i3', 'iC1', 'iC2']:
for suffix in ['t1', 't2']:
modality = prefix + '_' + suffix
fname = get_neobrain('test', idx, modality)
fname_noext = fname[:-4]
convert_neobrain_to_nifti(fname_noext)
def convert_all():
# Convert training data
for idx in range(1, 1 + 4):
for modality in ['T1', 'T2', 'seg']:
fname = get_neobrain('train', idx, modality)
fname_noext = fname[:-4]
convert_neobrain_to_nifti(fname_noext)
# Convert testing data
for idx in range(1, 1 + 3):
for prefix in ['i1', 'i2', 'i3', 'iC1', 'iC2']:
for suffix in ['t1', 't2']:
modality = prefix + '_' + suffix
fname = get_neobrain('test', idx, modality)
fname_noext = fname[:-4]
convert_neobrain_to_nifti(fname_noext)
def align_atlas():
neo_fname = get_neobrain('train', 1, 'T2')
neo_nib = nib.load(neo_fname)
neo = neo_nib.get_data()
neo_affine = neo_nib.get_affine()
# Load atlas (with skull)
atlas_fname = get_neobrain('atlas', 'neo-withSkull', None)
atlas_nib = nib.load(atlas_fname)
atlas = atlas_nib.get_data()
atlas_affine = atlas_nib.get_affine()
# The first training volume dimensions are about 5cm x 5cm x 8cm
# The atlas dimensions are about 7cm x 9cm x 11 cm
# Assuming isotropic scale, the atlas is about 1.5 times larger than
# the input image:
iso_scale = (float(7*9*11)/float(5*5*8))**(1.0/3)
print(iso_scale)
#We can use this to constraint the transformation to rigid
scale = np.eye(4)
scale[:3,:3] *= iso_scale
rigid_map_fname = 'atlas_towards_neo1_rigid.p'
if os.path.isfile(rigid_map_fname):
rigid_map = pickle.load(open(rigid_map_fname, 'r'))
else:
transforms = ['RIGID']
rigid_map = dipy_align(neo, neo_affine, atlas, atlas_affine,
transforms=transforms, prealign=scale)
pickle.dump(rigid_map, open(rigid_map_fname, 'w'))
atlas_resampled = rigid_map.transform(atlas)
# Compare anterior coronal slices
rt.overlay_slices(neo, atlas_resampled, slice_type=2, slice_index=10, ltitle='Neo1', rtitle='Atlas');
# Compare middle coronal slices
rt.overlay_slices(neo, atlas_resampled, slice_type=2, slice_index=25, ltitle='Neo1', rtitle='Atlas');
# Compare posterior coronal slices
rt.overlay_slices(neo, atlas_resampled, slice_type=2, slice_index=40, ltitle='Neo1', rtitle='Atlas');
# Load the peeled atlas
atlas_wcerebellum_fname = get_neobrain('atlas', 'neo-withCerebellum', None)
atlas_wcerebellum_nib = nib.load(atlas_wcerebellum_fname)
atlas_wcerebellum = atlas_wcerebellum_nib.get_data()
atlas_wcerebellum_affine = atlas_wcerebellum_nib.get_affine()
# Configure diffeomorphic registration
diff_map_name = 'atlas_towards_neo1_diff.p'
if os.path.isfile(diff_map_name):
diff_map = pickle.load(open(diff_map_name, 'r'))
else:
metric = CCMetric(3)
sdr = SymmetricDiffeomorphicRegistration(metric)
# The atlases are not aligned in physical space!! use atlas_affine instead of atlas_wcerebellum_affine
diff_map = sdr.optimize(neo, atlas_wcerebellum, neo_affine, atlas_affine, prealign=rigid_map.affine)
pickle.dump(diff_map, open(diff_map_name, 'w'))
atlas_wcerebellum_deformed = diff_map.transform(atlas_wcerebellum)
# Before and after diffeomorphic registration
rt.overlay_slices(neo, atlas_resampled, slice_type=2, slice_index=10, ltitle='Neo1', rtitle='Atlas');
rt.overlay_slices(neo, atlas_wcerebellum_deformed, slice_type=2, slice_index=10, ltitle='Neo1', rtitle='Atlas');
# Before and after diffeomorphic registration
rt.overlay_slices(neo, atlas_resampled, slice_type=2, slice_index=25, ltitle='Neo1', rtitle='Atlas');
rt.overlay_slices(neo, atlas_wcerebellum_deformed, slice_type=2, slice_index=25, ltitle='Neo1', rtitle='Atlas');
# Before and after diffeomorphic registration
rt.overlay_slices(neo, atlas_resampled, slice_type=2, slice_index=40, ltitle='Neo1', rtitle='Atlas');
rt.overlay_slices(neo, atlas_wcerebellum_deformed, slice_type=2, slice_index=40, ltitle='Neo1', rtitle='Atlas');
# Now all volumes
atlas_fname = get_neobrain('atlas', 'neo-withSkull', None)
atlas_nib = nib.load(atlas_fname)
atlas = atlas_nib.get_data()
atlas_affine = atlas_nib.get_affine()
atlas_wcerebellum_fname = get_neobrain('atlas', 'neo-withCerebellum', None)
atlas_wcerebellum_nib = nib.load(atlas_wcerebellum_fname)
atlas_wcerebellum = atlas_wcerebellum_nib.get_data()
atlas_wcerebellum_affine = atlas_wcerebellum_nib.get_affine()
idx = 2
neoi_fname = get_neobrain('train', idx, 'T2')
neoi_nib = nib.load(neoi_fname)
neoi = neoi_nib.get_data()
neoi_affine = neoi_nib.get_affine()
iso_scale = (float(7*9*11)/float(5*5*8))**(1.0/3)
print(iso_scale)
#We can use this to constraint the transformation to rigid
scale = np.eye(4)
scale[:3,:3] *= iso_scale
rigid_map_fname = 'atlas_towards_neo%d_affine.p'%(idx,)
if os.path.isfile(rigid_map_fname):
rigid_map = pickle.load(open(rigid_map_fname, 'r'))
else:
transforms = ['RIGID', 'AFFINE']
level_iters = [[10000, 1000, 100], [100]]
rigid_map = dipy_align(neoi, neoi_affine, atlas, atlas_affine,
transforms=transforms,
level_iters=level_iters,
prealign=scale)
pickle.dump(rigid_map, open(rigid_map_fname, 'w'))
atlas_resampled = rigid_map.transform(atlas)
rt.overlay_slices(neoi, atlas_resampled, slice_type=2, slice_index = 6)
rt.overlay_slices(neoi, atlas_resampled, slice_type=2, slice_index = 10)
rt.overlay_slices(neoi, atlas_resampled, slice_type=2, slice_index = 25)
rt.overlay_slices(neoi, atlas_resampled, slice_type=2, slice_index = 40)
diff_map_name = 'atlas_towards_neo%d_diff.p'%(idx,)
if os.path.isfile(diff_map_name):
diff_map = pickle.load(open(diff_map_name, 'r'))
else:
metric = CCMetric(3)
sdr = SymmetricDiffeomorphicRegistration(metric)
# The atlases are not aligned in physical space!! use atlas_affine instead of atlas_wcerebellum_affine
diff_map = sdr.optimize(neoi, atlas_wcerebellum, neoi_affine, atlas_affine, prealign=rigid_map.affine)
pickle.dump(diff_map, open(diff_map_name, 'w'))
atlas_wcerebellum_deformed = diff_map.transform(atlas_wcerebellum)
# Before and after diffeomorphic registration
rt.overlay_slices(neoi, atlas_resampled, slice_type=2, slice_index=6, ltitle='Neo%d'%(idx,), rtitle='Atlas');
rt.overlay_slices(neoi, atlas_wcerebellum_deformed, slice_type=2, slice_index=6, ltitle='Neo%d'%(idx,), rtitle='Atlas');
rt.overlay_slices(neoi, atlas_resampled, slice_type=2, slice_index=10, ltitle='Neo%d'%(idx,), rtitle='Atlas');
rt.overlay_slices(neoi, atlas_wcerebellum_deformed, slice_type=2, slice_index=10, ltitle='Neo%d'%(idx,), rtitle='Atlas');
# Before and after diffeomorphic registration
rt.overlay_slices(neoi, atlas_resampled, slice_type=2, slice_index=25, ltitle='Neo%d'%(idx,), rtitle='Atlas');
rt.overlay_slices(neoi, atlas_wcerebellum_deformed, slice_type=2, slice_index=25, ltitle='Neo%d'%(idx,), rtitle='Atlas');
# Before and after diffeomorphic registration
rt.overlay_slices(neoi, atlas_resampled, slice_type=2, slice_index=40, ltitle='Neo%d'%(idx,), rtitle='Atlas');
rt.overlay_slices(neoi, atlas_wcerebellum_deformed, slice_type=2, slice_index=40, ltitle='Neo%d'%(idx,), rtitle='Atlas');
def quick_check():
img1_fname = "/home/omar/data/DATA_NeoBrainS12/T1.nii.gz"
img2_fname = "/home/omar/data/DATA_NeoBrainS12/set2_i1_t1.nii.gz"
img1_nib = nib.load(img1_fname)
img1 = img1_nib.get_data().squeeze()
img1_affine = img1_nib.get_affine()
img2_nib = nib.load(img2_fname)
img2 = img2_nib.get_data().squeeze()
img2_affine = img2_nib.get_affine()
# nib.aff2axcodes(img1_affine)
#aff = AffineMap(None, img1.shape, img1_affine, img2.shape, img2_affine)
#aff = transform_centers_of_mass(img1, img1_affine, img2, img2_affine)
aff = dipy_align(img1, img1_affine, img2, img2_affine, np.eye(4))
img2_resampled = aff.transform(img2)
rt.overlay_slices(img1, img2_resampled, slice_type=0)
rt.overlay_slices(img1, img2_resampled, slice_type=1)
rt.overlay_slices(img1, img2_resampled, slice_type=2)
# Verify that original and RAS versions of neo1 describe the same object
# Load original data
neo1_fname = get_neobrain('train', 1, 'T1')
neo1_old, neo1_old_affine, neo1_old_spacing, neo1_old_ori = load_from_raw(neo1_fname)
# Load RAS version
neo1_nib = nib.load(neo1_fname)
neo1 = neo1_nib.get_data()
neo1_affine = neo1_nib.get_affine()
# Resample RAS on top of original
aff = AffineMap(None, neo1_old.shape, neo1_old_affine, neo1.shape, neo1_affine)
neo1_resampled = aff.transform(neo1)
rt.overlay_slices(neo1_old, neo1_resampled, slice_type=0)
rt.overlay_slices(neo1_old, neo1_resampled, slice_type=1)
rt.overlay_slices(neo1_old, neo1_resampled, slice_type=2)
# Attempt to resample a test volume on top of training
neo2_fname = get_neobrain('test', 1, 'i1_t1')
neo2_nib = nib.load(neo2_fname)
neo2 = neo2_nib.get_data()
neo2_affine = neo2_nib.get_affine()
aff = transform_centers_of_mass(neo1, neo1_affine, neo2, neo2_affine)
#aff = dipy_align(neo1, neo1_affine, neo2, neo2_affine)
neo2_resampled = aff.transform(neo2)
rt.overlay_slices(neo1, neo2_resampled, slice_type=0)
rt.overlay_slices(neo1, neo2_resampled, slice_type=1)
rt.overlay_slices(neo1, neo2_resampled, slice_type=2)
# Load atlas
atlas_fname = get_neobrain('atlas', 'neo-withSkull', None)
atlas_nib = nib.load(atlas_fname)
atlas_affine = atlas_nib.get_affine()
atlas = atlas_nib.get_data()
rt.plot_slices(atlas)
# Resample atlas on top of neo1
aff = AffineMap(None, neo1.shape, neo1_affine, atlas.shape, atlas_affine)
atlas_resampled = aff.transform(atlas)
rt.overlay_slices(neo1, atlas_resampled)
def align_atlas():
neo_fname = get_neobrain('train', 1, 'T2')
neo_nib = nib.load(neo_fname)
neo = neo_nib.get_data()
neo_affine = neo_nib.get_affine()
# Load atlas (with skull)
atlas_fname = get_neobrain('atlas', 'neo-withSkull', None)
atlas_nib = nib.load(atlas_fname)
atlas = atlas_nib.get_data()
atlas_affine = atlas_nib.get_affine()
# The first training volume dimensions are about 5cm x 5cm x 8cm
# The atlas dimensions are about 7cm x 9cm x 11 cm
# Assuming isotropic scale, the atlas is about 1.5 times larger than
# the input image:
iso_scale = (float(7 * 9 * 11) / float(5 * 5 * 8))**(1.0 / 3)
print(iso_scale)
#We can use this to constraint the transformation to rigid
scale = np.eye(4)
scale[:3, :3] *= iso_scale
rigid_map_fname = 'atlas_towards_neo1_rigid.p'
if os.path.isfile(rigid_map_fname):
rigid_map = pickle.load(open(rigid_map_fname, 'r'))
else:
transforms = ['RIGID']
rigid_map = dipy_align(neo,
neo_affine,
atlas,
atlas_affine,
transforms=transforms,
prealign=scale)
pickle.dump(rigid_map, open(rigid_map_fname, 'w'))
atlas_resampled = rigid_map.transform(atlas)
# Compare anterior coronal slices
rt.overlay_slices(neo,
atlas_resampled,
slice_type=2,
slice_index=10,
ltitle='Neo1',
rtitle='Atlas')
# Compare middle coronal slices
rt.overlay_slices(neo,
atlas_resampled,
slice_type=2,
slice_index=25,
ltitle='Neo1',
rtitle='Atlas')
# Compare posterior coronal slices
rt.overlay_slices(neo,
atlas_resampled,
slice_type=2,
slice_index=40,
ltitle='Neo1',
rtitle='Atlas')
# Load the peeled atlas
atlas_wcerebellum_fname = get_neobrain('atlas', 'neo-withCerebellum', None)
atlas_wcerebellum_nib = nib.load(atlas_wcerebellum_fname)
atlas_wcerebellum = atlas_wcerebellum_nib.get_data()
atlas_wcerebellum_affine = atlas_wcerebellum_nib.get_affine()
# Configure diffeomorphic registration
diff_map_name = 'atlas_towards_neo1_diff.p'
if os.path.isfile(diff_map_name):
diff_map = pickle.load(open(diff_map_name, 'r'))
else:
metric = CCMetric(3)
sdr = SymmetricDiffeomorphicRegistration(metric)
# The atlases are not aligned in physical space!! use atlas_affine instead of atlas_wcerebellum_affine
diff_map = sdr.optimize(neo,
atlas_wcerebellum,
neo_affine,
atlas_affine,
prealign=rigid_map.affine)
pickle.dump(diff_map, open(diff_map_name, 'w'))
atlas_wcerebellum_deformed = diff_map.transform(atlas_wcerebellum)
# Before and after diffeomorphic registration
rt.overlay_slices(neo,
atlas_resampled,
slice_type=2,
slice_index=10,
ltitle='Neo1',
rtitle='Atlas')
rt.overlay_slices(neo,
atlas_wcerebellum_deformed,
slice_type=2,
slice_index=10,
ltitle='Neo1',
rtitle='Atlas')
# Before and after diffeomorphic registration
rt.overlay_slices(neo,
atlas_resampled,
slice_type=2,
slice_index=25,
ltitle='Neo1',
rtitle='Atlas')
rt.overlay_slices(neo,
atlas_wcerebellum_deformed,
slice_type=2,
slice_index=25,
ltitle='Neo1',
rtitle='Atlas')
# Before and after diffeomorphic registration
rt.overlay_slices(neo,
atlas_resampled,
slice_type=2,
slice_index=40,
ltitle='Neo1',
rtitle='Atlas')
rt.overlay_slices(neo,
atlas_wcerebellum_deformed,
slice_type=2,
slice_index=40,
ltitle='Neo1',
rtitle='Atlas')
# Now all volumes
atlas_fname = get_neobrain('atlas', 'neo-withSkull', None)
atlas_nib = nib.load(atlas_fname)
atlas = atlas_nib.get_data()
atlas_affine = atlas_nib.get_affine()
atlas_wcerebellum_fname = get_neobrain('atlas', 'neo-withCerebellum', None)
atlas_wcerebellum_nib = nib.load(atlas_wcerebellum_fname)
atlas_wcerebellum = atlas_wcerebellum_nib.get_data()
atlas_wcerebellum_affine = atlas_wcerebellum_nib.get_affine()
idx = 2
neoi_fname = get_neobrain('train', idx, 'T2')
neoi_nib = nib.load(neoi_fname)
neoi = neoi_nib.get_data()
neoi_affine = neoi_nib.get_affine()
iso_scale = (float(7 * 9 * 11) / float(5 * 5 * 8))**(1.0 / 3)
print(iso_scale)
#We can use this to constraint the transformation to rigid
scale = np.eye(4)
scale[:3, :3] *= iso_scale
rigid_map_fname = 'atlas_towards_neo%d_affine.p' % (idx, )
if os.path.isfile(rigid_map_fname):
rigid_map = pickle.load(open(rigid_map_fname, 'r'))
else:
transforms = ['RIGID', 'AFFINE']
level_iters = [[10000, 1000, 100], [100]]
rigid_map = dipy_align(neoi,
neoi_affine,
atlas,
atlas_affine,
transforms=transforms,
level_iters=level_iters,
prealign=scale)
pickle.dump(rigid_map, open(rigid_map_fname, 'w'))
atlas_resampled = rigid_map.transform(atlas)
rt.overlay_slices(neoi, atlas_resampled, slice_type=2, slice_index=6)
rt.overlay_slices(neoi, atlas_resampled, slice_type=2, slice_index=10)
rt.overlay_slices(neoi, atlas_resampled, slice_type=2, slice_index=25)
rt.overlay_slices(neoi, atlas_resampled, slice_type=2, slice_index=40)
diff_map_name = 'atlas_towards_neo%d_diff.p' % (idx, )
if os.path.isfile(diff_map_name):
diff_map = pickle.load(open(diff_map_name, 'r'))
else:
metric = CCMetric(3)
sdr = SymmetricDiffeomorphicRegistration(metric)
# The atlases are not aligned in physical space!! use atlas_affine instead of atlas_wcerebellum_affine
diff_map = sdr.optimize(neoi,
atlas_wcerebellum,
neoi_affine,
atlas_affine,
prealign=rigid_map.affine)
pickle.dump(diff_map, open(diff_map_name, 'w'))
atlas_wcerebellum_deformed = diff_map.transform(atlas_wcerebellum)
# Before and after diffeomorphic registration
rt.overlay_slices(neoi,
atlas_resampled,
slice_type=2,
slice_index=6,
ltitle='Neo%d' % (idx, ),
rtitle='Atlas')
rt.overlay_slices(neoi,
atlas_wcerebellum_deformed,
slice_type=2,
slice_index=6,
ltitle='Neo%d' % (idx, ),
rtitle='Atlas')
rt.overlay_slices(neoi,
atlas_resampled,
slice_type=2,
slice_index=10,
ltitle='Neo%d' % (idx, ),
rtitle='Atlas')
rt.overlay_slices(neoi,
atlas_wcerebellum_deformed,
slice_type=2,
slice_index=10,
ltitle='Neo%d' % (idx, ),
rtitle='Atlas')
# Before and after diffeomorphic registration
rt.overlay_slices(neoi,
atlas_resampled,
slice_type=2,
slice_index=25,
ltitle='Neo%d' % (idx, ),
rtitle='Atlas')
rt.overlay_slices(neoi,
atlas_wcerebellum_deformed,
slice_type=2,
slice_index=25,
ltitle='Neo%d' % (idx, ),
rtitle='Atlas')
# Before and after diffeomorphic registration
rt.overlay_slices(neoi,
atlas_resampled,
slice_type=2,
slice_index=40,
ltitle='Neo%d' % (idx, ),
rtitle='Atlas')
rt.overlay_slices(neoi,
atlas_wcerebellum_deformed,
slice_type=2,
slice_index=40,
ltitle='Neo%d' % (idx, ),
rtitle='Atlas')
def quick_check():
img1_fname = "/home/omar/data/DATA_NeoBrainS12/T1.nii.gz"
img2_fname = "/home/omar/data/DATA_NeoBrainS12/set2_i1_t1.nii.gz"
img1_nib = nib.load(img1_fname)
img1 = img1_nib.get_data().squeeze()
img1_affine = img1_nib.get_affine()
img2_nib = nib.load(img2_fname)
img2 = img2_nib.get_data().squeeze()
img2_affine = img2_nib.get_affine()
# nib.aff2axcodes(img1_affine)
#aff = AffineMap(None, img1.shape, img1_affine, img2.shape, img2_affine)
#aff = transform_centers_of_mass(img1, img1_affine, img2, img2_affine)
aff = dipy_align(img1, img1_affine, img2, img2_affine, np.eye(4))
img2_resampled = aff.transform(img2)
rt.overlay_slices(img1, img2_resampled, slice_type=0)
rt.overlay_slices(img1, img2_resampled, slice_type=1)
rt.overlay_slices(img1, img2_resampled, slice_type=2)
# Verify that original and RAS versions of neo1 describe the same object
# Load original data
neo1_fname = get_neobrain('train', 1, 'T1')
neo1_old, neo1_old_affine, neo1_old_spacing, neo1_old_ori = load_from_raw(
neo1_fname)
# Load RAS version
neo1_nib = nib.load(neo1_fname)
neo1 = neo1_nib.get_data()
neo1_affine = neo1_nib.get_affine()
# Resample RAS on top of original
aff = AffineMap(None, neo1_old.shape, neo1_old_affine, neo1.shape,
neo1_affine)
neo1_resampled = aff.transform(neo1)
rt.overlay_slices(neo1_old, neo1_resampled, slice_type=0)
rt.overlay_slices(neo1_old, neo1_resampled, slice_type=1)
rt.overlay_slices(neo1_old, neo1_resampled, slice_type=2)
# Attempt to resample a test volume on top of training
neo2_fname = get_neobrain('test', 1, 'i1_t1')
neo2_nib = nib.load(neo2_fname)
neo2 = neo2_nib.get_data()
neo2_affine = neo2_nib.get_affine()
aff = transform_centers_of_mass(neo1, neo1_affine, neo2, neo2_affine)
#aff = dipy_align(neo1, neo1_affine, neo2, neo2_affine)
neo2_resampled = aff.transform(neo2)
rt.overlay_slices(neo1, neo2_resampled, slice_type=0)
rt.overlay_slices(neo1, neo2_resampled, slice_type=1)
rt.overlay_slices(neo1, neo2_resampled, slice_type=2)
# Load atlas
atlas_fname = get_neobrain('atlas', 'neo-withSkull', None)
atlas_nib = nib.load(atlas_fname)
atlas_affine = atlas_nib.get_affine()
atlas = atlas_nib.get_data()
rt.plot_slices(atlas)
# Resample atlas on top of neo1
aff = AffineMap(None, neo1.shape, neo1_affine, atlas.shape, atlas_affine)
atlas_resampled = aff.transform(atlas)
rt.overlay_slices(neo1, atlas_resampled)
