def test_uncrop_image():
input_shape = (100, 100, 100)
crop_size = 20
data_crop = np.random.randint(0, 2, size=(crop_size, crop_size, input_shape[2]))
data_in = np.random.randint(0, 1000, size=input_shape)
x_crop_lst = list(np.random.randint(0, input_shape[0]-crop_size, input_shape[2]))
y_crop_lst = list(np.random.randint(0,input_shape[1]-crop_size, input_shape[2]))
z_crop_lst = range(input_shape[2])
affine = np.eye(4)
nii = nib.nifti1.Nifti1Image(data_in, affine)
img_in = Image(data_in, hdr=nii.header, dim=nii.header.get_data_shape())
img_uncrop = deepseg_sc.uncrop_image(ref_in=img_in,
data_crop=data_crop,
x_crop_lst=x_crop_lst,
y_crop_lst=y_crop_lst,
z_crop_lst=z_crop_lst)
assert img_uncrop.data.shape == input_shape
z_rand = np.random.randint(0, input_shape[2])
assert np.allclose(img_uncrop.data[x_crop_lst[z_rand]:x_crop_lst[z_rand]+crop_size,
y_crop_lst[z_rand]:y_crop_lst[z_rand]+crop_size,
z_rand],
data_crop[:, :, z_rand])
def test_uncrop_image():
input_shape = (100, 100, 100)
crop_size = 20
data_crop = np.random.randint(0,
2,
size=(crop_size, crop_size, input_shape[2]))
data_in = np.random.randint(0, 1000, size=input_shape)
x_crop_lst = list(
np.random.randint(0, input_shape[0] - crop_size, input_shape[2]))
y_crop_lst = list(
np.random.randint(0, input_shape[1] - crop_size, input_shape[2]))
z_crop_lst = range(input_shape[2])
affine = np.eye(4)
nii = nib.nifti1.Nifti1Image(data_in, affine)
img_in = Image(data_in, hdr=nii.header, dim=nii.header.get_data_shape())
img_uncrop = deepseg_sc.uncrop_image(ref_in=img_in,
data_crop=data_crop,
x_crop_lst=x_crop_lst,
y_crop_lst=y_crop_lst,
z_crop_lst=z_crop_lst)
assert img_uncrop.data.shape == input_shape
z_rand = np.random.randint(0, input_shape[2])
assert np.allclose(
img_uncrop.data[x_crop_lst[z_rand]:x_crop_lst[z_rand] + crop_size,
y_crop_lst[z_rand]:y_crop_lst[z_rand] + crop_size,
z_rand], data_crop[:, :, z_rand])
def deep_segmentation_MSlesion(im_image,
contrast_type,
ctr_algo='svm',
ctr_file=None,
brain_bool=True,
remove_temp_files=1,
verbose=1):
"""
Segment lesions from MRI data.
:param im_image: Image() object containing the lesions to segment
:param contrast_type: Constrast of the image. Need to use one supported by the CNN models.
:param ctr_algo: Algo to find the centerline. See sct_get_centerline
:param ctr_file: Centerline or segmentation (optional)
:param brain_bool: If brain if present or not in the image.
:param remove_temp_files:
:return:
"""
# create temporary folder with intermediate results
tmp_folder = sct.TempFolder(verbose=verbose)
tmp_folder_path = tmp_folder.get_path()
if ctr_algo == 'file': # if the ctr_file is provided
tmp_folder.copy_from(ctr_file)
file_ctr = os.path.basename(ctr_file)
else:
file_ctr = None
tmp_folder.chdir()
fname_in = im_image.absolutepath
# re-orient image to RPI
logger.info("Reorient the image to RPI, if necessary...")
original_orientation = im_image.orientation
# fname_orient = 'image_in_RPI.nii'
im_image.change_orientation('RPI')
input_resolution = im_image.dim[4:7]
# Resample image to 0.5mm in plane
im_image_res = \
resampling.resample_nib(im_image, new_size=[0.5, 0.5, im_image.dim[6]], new_size_type='mm', interpolation='linear')
fname_orient = 'image_in_RPI_res.nii'
im_image_res.save(fname_orient)
# find the spinal cord centerline - execute OptiC binary
logger.info("\nFinding the spinal cord centerline...")
contrast_type_ctr = contrast_type.split('_')[0]
_, im_ctl, im_labels_viewer = find_centerline(
algo=ctr_algo,
image_fname=fname_orient,
contrast_type=contrast_type_ctr,
brain_bool=brain_bool,
folder_output=tmp_folder_path,
remove_temp_files=remove_temp_files,
centerline_fname=file_ctr)
if ctr_algo == 'file':
im_ctl = \
resampling.resample_nib(im_ctl, new_size=[0.5, 0.5, im_image.dim[6]], new_size_type='mm', interpolation='linear')
# crop image around the spinal cord centerline
logger.info("\nCropping the image around the spinal cord...")
crop_size = 48
X_CROP_LST, Y_CROP_LST, Z_CROP_LST, im_crop_nii = crop_image_around_centerline(
im_in=im_image_res, ctr_in=im_ctl, crop_size=crop_size)
del im_ctl
# normalize the intensity of the images
logger.info("Normalizing the intensity...")
im_norm_in = apply_intensity_normalization(img=im_crop_nii,
contrast=contrast_type)
del im_crop_nii
# resample to 0.5mm isotropic
fname_norm = sct.add_suffix(fname_orient, '_norm')
im_norm_in.save(fname_norm)
fname_res3d = sct.add_suffix(fname_norm, '_resampled3d')
resampling.resample_file(fname_norm,
fname_res3d,
'0.5x0.5x0.5',
'mm',
'linear',
verbose=0)
# segment data using 3D convolutions
logger.info(
"\nSegmenting the MS lesions using deep learning on 3D patches...")
segmentation_model_fname = sct_dir_local_path(
'data', 'deepseg_lesion_models', '{}_lesion.h5'.format(contrast_type))
fname_seg_crop_res = sct.add_suffix(fname_res3d, '_lesionseg')
im_res3d = Image(fname_res3d)
seg_im = segment_3d(model_fname=segmentation_model_fname,
contrast_type=contrast_type,
im=im_res3d.copy())
seg_im.save(fname_seg_crop_res)
del im_res3d, seg_im
# resample to the initial pz resolution
fname_seg_res2d = sct.add_suffix(fname_seg_crop_res, '_resampled2d')
initial_2d_resolution = 'x'.join(['0.5', '0.5', str(input_resolution[2])])
resampling.resample_file(fname_seg_crop_res,
fname_seg_res2d,
initial_2d_resolution,
'mm',
'linear',
verbose=0)
seg_crop = Image(fname_seg_res2d)
# reconstruct the segmentation from the crop data
logger.info("\nReassembling the image...")
seg_uncrop_nii = uncrop_image(ref_in=im_image_res,
data_crop=seg_crop.copy().data,
x_crop_lst=X_CROP_LST,
y_crop_lst=Y_CROP_LST,
z_crop_lst=Z_CROP_LST)
fname_seg_res_RPI = sct.add_suffix(fname_in, '_res_RPI_seg')
seg_uncrop_nii.save(fname_seg_res_RPI)
del seg_crop
# resample to initial resolution
logger.info(
"Resampling the segmentation to the original image resolution...")
initial_resolution = 'x'.join([
str(input_resolution[0]),
str(input_resolution[1]),
str(input_resolution[2])
])
fname_seg_RPI = sct.add_suffix(fname_in, '_RPI_seg')
resampling.resample_file(fname_seg_res_RPI,
fname_seg_RPI,
initial_resolution,
'mm',
'linear',
verbose=0)
seg_initres_nii = Image(fname_seg_RPI)
if ctr_algo == 'viewer': # resample and reorient the viewer labels
im_labels_viewer_nib = nib.nifti1.Nifti1Image(
im_labels_viewer.data, im_labels_viewer.hdr.get_best_affine())
im_viewer_r_nib = resampling.resample_nib(im_labels_viewer_nib,
new_size=input_resolution,
new_size_type='mm',
interpolation='linear')
im_viewer = Image(
im_viewer_r_nib.get_data(),
hdr=im_viewer_r_nib.header,
orientation='RPI',
dim=im_viewer_r_nib.header.get_data_shape()).change_orientation(
original_orientation)
else:
im_viewer = None
if verbose == 2:
fname_res_ctr = sct.add_suffix(fname_orient, '_ctr')
resampling.resample_file(fname_res_ctr,
fname_res_ctr,
initial_resolution,
'mm',
'linear',
verbose=0)
im_image_res_ctr_downsamp = Image(fname_res_ctr).change_orientation(
original_orientation)
else:
im_image_res_ctr_downsamp = None
# binarize the resampled image to remove interpolation effects
logger.info(
"\nBinarizing the segmentation to avoid interpolation effects...")
thr = 0.1
seg_initres_nii.data[np.where(seg_initres_nii.data >= thr)] = 1
seg_initres_nii.data[np.where(seg_initres_nii.data < thr)] = 0
# change data type
seg_initres_nii.change_type(np.uint8)
# reorient to initial orientation
logger.info(
"\nReorienting the segmentation to the original image orientation...")
tmp_folder.chdir_undo()
# remove temporary files
if remove_temp_files:
logger.info("\nRemove temporary files...")
tmp_folder.cleanup()
# reorient to initial orientation
return seg_initres_nii.change_orientation(
original_orientation), im_viewer, im_image_res_ctr_downsamp
def deep_segmentation_MSlesion(im_image, contrast_type, ctr_algo='svm', ctr_file=None, brain_bool=True,
remove_temp_files=1, verbose=1):
"""
Segment lesions from MRI data.
:param im_image: Image() object containing the lesions to segment
:param contrast_type: Constrast of the image. Need to use one supported by the CNN models.
:param ctr_algo: Algo to find the centerline. See sct_get_centerline
:param ctr_file: Centerline or segmentation (optional)
:param brain_bool: If brain if present or not in the image.
:param remove_temp_files:
:return:
"""
# create temporary folder with intermediate results
tmp_folder = sct.TempFolder(verbose=verbose)
tmp_folder_path = tmp_folder.get_path()
if ctr_algo == 'file': # if the ctr_file is provided
tmp_folder.copy_from(ctr_file)
file_ctr = os.path.basename(ctr_file)
else:
file_ctr = None
tmp_folder.chdir()
# orientation of the image, should be RPI
logger.info("\nReorient the image to RPI, if necessary...")
fname_in = im_image.absolutepath
original_orientation = im_image.orientation
fname_orient = 'image_in_RPI.nii'
im_image.change_orientation('RPI').save(fname_orient)
input_resolution = im_image.dim[4:7]
# find the spinal cord centerline - execute OptiC binary
logger.info("\nFinding the spinal cord centerline...")
contrast_type_ctr = contrast_type.split('_')[0]
fname_res, centerline_filename = find_centerline(algo=ctr_algo,
image_fname=fname_orient,
contrast_type=contrast_type_ctr,
brain_bool=brain_bool,
folder_output=tmp_folder_path,
remove_temp_files=remove_temp_files,
centerline_fname=file_ctr)
im_nii, ctr_nii = Image(fname_res), Image(centerline_filename)
# crop image around the spinal cord centerline
logger.info("\nCropping the image around the spinal cord...")
crop_size = 48
X_CROP_LST, Y_CROP_LST, Z_CROP_LST, im_crop_nii = crop_image_around_centerline(im_in=im_nii,
ctr_in=ctr_nii,
crop_size=crop_size)
del ctr_nii
# normalize the intensity of the images
logger.info("Normalizing the intensity...")
im_norm_in = apply_intensity_normalization(img=im_crop_nii, contrast=contrast_type)
del im_crop_nii
# resample to 0.5mm isotropic
fname_norm = sct.add_suffix(fname_orient, '_norm')
im_norm_in.save(fname_norm)
fname_res3d = sct.add_suffix(fname_norm, '_resampled3d')
resampling.resample_file(fname_norm, fname_res3d, '0.5x0.5x0.5', 'mm', 'linear',
verbose=0)
# segment data using 3D convolutions
logger.info("\nSegmenting the MS lesions using deep learning on 3D patches...")
segmentation_model_fname = os.path.join(sct.__sct_dir__, 'data', 'deepseg_lesion_models',
'{}_lesion.h5'.format(contrast_type))
fname_seg_crop_res = sct.add_suffix(fname_res3d, '_lesionseg')
im_res3d = Image(fname_res3d)
seg_im = segment_3d(model_fname=segmentation_model_fname,
contrast_type=contrast_type,
im=im_res3d.copy())
seg_im.save(fname_seg_crop_res)
del im_res3d, seg_im
# resample to the initial pz resolution
fname_seg_res2d = sct.add_suffix(fname_seg_crop_res, '_resampled2d')
initial_2d_resolution = 'x'.join(['0.5', '0.5', str(input_resolution[2])])
resampling.resample_file(fname_seg_crop_res, fname_seg_res2d, initial_2d_resolution,
'mm', 'linear', verbose=0)
seg_crop = Image(fname_seg_res2d)
# reconstruct the segmentation from the crop data
logger.info("\nReassembling the image...")
seg_uncrop_nii = uncrop_image(ref_in=im_nii, data_crop=seg_crop.copy().data, x_crop_lst=X_CROP_LST,
y_crop_lst=Y_CROP_LST, z_crop_lst=Z_CROP_LST)
fname_seg_res_RPI = sct.add_suffix(fname_in, '_res_RPI_seg')
seg_uncrop_nii.save(fname_seg_res_RPI)
del seg_crop
# resample to initial resolution
logger.info("Resampling the segmentation to the original image resolution...")
initial_resolution = 'x'.join([str(input_resolution[0]), str(input_resolution[1]), str(input_resolution[2])])
fname_seg_RPI = sct.add_suffix(fname_in, '_RPI_seg')
resampling.resample_file(fname_seg_res_RPI, fname_seg_RPI, initial_resolution,
'mm', 'linear', verbose=0)
seg_initres_nii = Image(fname_seg_RPI)
if ctr_algo == 'viewer': # resample and reorient the viewer labels
fname_res_labels = sct.add_suffix(fname_orient, '_labels-centerline')
resampling.resample_file(fname_res_labels, fname_res_labels, initial_resolution,
'mm', 'linear', verbose=0)
im_image_res_labels_downsamp = Image(fname_res_labels).change_orientation(original_orientation)
else:
im_image_res_labels_downsamp = None
if verbose == 2:
fname_res_ctr = sct.add_suffix(fname_orient, '_ctr')
resampling.resample_file(fname_res_ctr, fname_res_ctr, initial_resolution,
'mm', 'linear', verbose=0)
im_image_res_ctr_downsamp = Image(fname_res_ctr).change_orientation(original_orientation)
else:
im_image_res_ctr_downsamp = None
# binarize the resampled image to remove interpolation effects
logger.info("\nBinarizing the segmentation to avoid interpolation effects...")
thr = 0.1
seg_initres_nii.data[np.where(seg_initres_nii.data >= thr)] = 1
seg_initres_nii.data[np.where(seg_initres_nii.data < thr)] = 0
# reorient to initial orientation
logger.info("\nReorienting the segmentation to the original image orientation...")
tmp_folder.chdir_undo()
# remove temporary files
if remove_temp_files:
logger.info("\nRemove temporary files...")
tmp_folder.cleanup()
# reorient to initial orientation
return seg_initres_nii.change_orientation(original_orientation), im_image_res_labels_downsamp, im_image_res_ctr_downsamp
def deep_segmentation_MSlesion(im_image,
contrast_type,
ctr_algo='svm',
ctr_file=None,
brain_bool=True,
remove_temp_files=1):
"""
Segment lesions from MRI data.
:param im_image: Image() object containing the lesions to segment
:param contrast_type: Constrast of the image. Need to use one supported by the CNN models.
:param ctr_algo: Algo to find the centerline. See sct_get_centerline
:param ctr_file: Centerline or segmentation (optional)
:param brain_bool: If brain if present or not in the image.
:param remove_temp_files:
:return:
"""
# create temporary folder with intermediate results
sct.log.info("\nCreating temporary folder...")
tmp_folder = sct.TempFolder()
tmp_folder_path = tmp_folder.get_path()
if ctr_algo == 'manual': # if the ctr_file is provided
tmp_folder.copy_from(ctr_file)
file_ctr = os.path.basename(ctr_file)
else:
file_ctr = None
tmp_folder.chdir()
# orientation of the image, should be RPI
sct.log.info("\nReorient the image to RPI, if necessary...")
fname_in = im_image.absolutepath
original_orientation = im_image.orientation
fname_orient = 'image_in_RPI.nii'
im_image.change_orientation('RPI').save(fname_orient)
input_resolution = im_image.dim[4:7]
# find the spinal cord centerline - execute OptiC binary
sct.log.info("\nFinding the spinal cord centerline...")
contrast_type_ctr = contrast_type.split('_')[0]
fname_res, centerline_filename = find_centerline(
algo=ctr_algo,
image_fname=fname_orient,
contrast_type=contrast_type_ctr,
brain_bool=brain_bool,
folder_output=tmp_folder_path,
remove_temp_files=remove_temp_files,
centerline_fname=file_ctr)
im_nii, ctr_nii = Image(fname_res), Image(centerline_filename)
# crop image around the spinal cord centerline
sct.log.info("\nCropping the image around the spinal cord...")
crop_size = 48
X_CROP_LST, Y_CROP_LST, Z_CROP_LST, im_crop_nii = crop_image_around_centerline(
im_in=im_nii, ctr_in=ctr_nii, crop_size=crop_size)
del ctr_nii
# normalize the intensity of the images
sct.log.info("Normalizing the intensity...")
im_norm_in = apply_intensity_normalization(img=im_crop_nii,
contrast=contrast_type)
del im_crop_nii
# resample to 0.5mm isotropic
fname_norm = sct.add_suffix(fname_orient, '_norm')
im_norm_in.save(fname_norm)
fname_res3d = sct.add_suffix(fname_norm, '_resampled3d')
resampling.resample_file(fname_norm,
fname_res3d,
'0.5x0.5x0.5',
'mm',
'linear',
verbose=0)
# segment data using 3D convolutions
sct.log.info(
"\nSegmenting the MS lesions using deep learning on 3D patches...")
segmentation_model_fname = os.path.join(
sct.__sct_dir__, 'data', 'deepseg_lesion_models',
'{}_lesion.h5'.format(contrast_type))
fname_seg_crop_res = sct.add_suffix(fname_res3d, '_lesionseg')
im_res3d = Image(fname_res3d)
seg_im = segment_3d(model_fname=segmentation_model_fname,
contrast_type=contrast_type,
im=im_res3d.copy())
seg_im.save(fname_seg_crop_res)
del im_res3d, seg_im
# resample to the initial pz resolution
fname_seg_res2d = sct.add_suffix(fname_seg_crop_res, '_resampled2d')
initial_2d_resolution = 'x'.join(['0.5', '0.5', str(input_resolution[2])])
resampling.resample_file(fname_seg_crop_res,
fname_seg_res2d,
initial_2d_resolution,
'mm',
'linear',
verbose=0)
seg_crop = Image(fname_seg_res2d)
# reconstruct the segmentation from the crop data
sct.log.info("\nReassembling the image...")
seg_uncrop_nii = uncrop_image(ref_in=im_nii,
data_crop=seg_crop.copy().data,
x_crop_lst=X_CROP_LST,
y_crop_lst=Y_CROP_LST,
z_crop_lst=Z_CROP_LST)
fname_seg_res_RPI = sct.add_suffix(fname_in, '_res_RPI_seg')
seg_uncrop_nii.save(fname_seg_res_RPI)
del seg_crop
# resample to initial resolution
sct.log.info(
"Resampling the segmentation to the original image resolution...")
initial_resolution = 'x'.join([
str(input_resolution[0]),
str(input_resolution[1]),
str(input_resolution[2])
])
fname_seg_RPI = sct.add_suffix(fname_in, '_RPI_seg')
resampling.resample_file(fname_seg_res_RPI,
fname_seg_RPI,
initial_resolution,
'mm',
'linear',
verbose=0)
seg_initres_nii = Image(fname_seg_RPI)
# binarize the resampled image to remove interpolation effects
sct.log.info(
"\nBinarizing the segmentation to avoid interpolation effects...")
thr = 0.1
seg_initres_nii.data[np.where(seg_initres_nii.data >= thr)] = 1
seg_initres_nii.data[np.where(seg_initres_nii.data < thr)] = 0
# reorient to initial orientation
sct.log.info(
"\nReorienting the segmentation to the original image orientation...")
tmp_folder.chdir_undo()
# remove temporary files
if remove_temp_files:
sct.log.info("\nRemove temporary files...")
tmp_folder.cleanup()
# reorient to initial orientation
return seg_initres_nii.change_orientation(original_orientation)