def test_get_centerline_optic():
"""Test extraction of metrics aggregation across slices: All slices by default"""
fname_t2 = os.path.join(__sct_dir__, 'sct_testing_data/t2/t2.nii.gz'
) # install: sct_download_data -d sct_testing_data
img_t2 = Image(fname_t2)
# Add non-numerical values at the top corner of the image for testing purpose
img_t2.change_type('float32')
img_t2.data[0, 0, 0] = np.nan
img_t2.data[1, 0, 0] = np.inf
img_out, arr_out, _, _ = get_centerline(img_t2,
ParamCenterline(
algo_fitting='optic',
contrast='t2',
minmax=False),
verbose=VERBOSE)
# Open ground truth segmentation and compare
fname_t2_seg = os.path.join(__sct_dir__,
'sct_testing_data/t2/t2_seg.nii.gz')
img_seg_out, arr_seg_out, _, _ = get_centerline(Image(fname_t2_seg),
ParamCenterline(
algo_fitting='bspline',
minmax=False),
verbose=VERBOSE)
assert np.linalg.norm(
find_and_sort_coord(img_seg_out) - find_and_sort_coord(img_out)) < 3.5
def test_get_centerline_optic():
"""Test extraction of metrics aggregation across slices: All slices by default"""
fname_t2 = os.path.join(sct.__sct_dir__, 'sct_testing_data/t2/t2.nii.gz') # install: sct_download_data -d sct_testing_data
img_t2 = Image(fname_t2)
# Add non-numerical values at the top corner of the image for testing purpose
img_t2.change_type('float32')
img_t2.data[0, 0, 0] = np.nan
img_t2.data[1, 0, 0] = np.inf
img_out, arr_out, _ = get_centerline(img_t2, algo_fitting='optic', contrast='t2', minmax=False, verbose=VERBOSE)
# Open ground truth segmentation and compare
fname_t2_seg = os.path.join(sct.__sct_dir__, 'sct_testing_data/t2/t2_seg.nii.gz')
img_seg_out, arr_seg_out, _ = get_centerline(Image(fname_t2_seg), algo_fitting='bspline', minmax=False,
verbose=VERBOSE)
assert np.linalg.norm(find_and_sort_coord(img_seg_out) - find_and_sort_coord(img_out)) < 3.5
def apply_intensity_normalization(img_path, fname_out, params=None):
"""Standardize the intensity range."""
img = Image(img_path)
img_normalized = img.change_type(np.float32)
img_normalized.data = scale_intensity(img_normalized.data)
img_normalized.save(fname_out)
def test_get_centerline_optic(params):
"""Test centerline extraction with optic"""
# TODO: add assert on the output .csv files for more precision
im = Image(params['fname_image'])
# Add non-numerical values at the top corner of the image for testing purpose
im.change_type('float32')
im.data[0, 0, 0] = np.nan
im.data[1, 0, 0] = np.inf
im_centerline, arr_out, _, _ = get_centerline(
im,
ParamCenterline(algo_fitting='optic',
contrast=params['contrast'],
minmax=False),
verbose=VERBOSE)
# Compare with ground truth centerline
assert np.all(
im_centerline.data == Image(params['fname_centerline-optic']).data)
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
