def label_with_dilation(self, to_label_nii_fname: os.PathLike, dilated_nii_fname: os.PathLike, out_nii_fname: os.PathLike): """ Labels a volume using its labeled dilation. The dilated volume is labeled using scipy.ndimage.label function. :param to_label_nii_fname: usually a CT-mask.nii.gz :param dilated_nii_fname: dilated version of the to_label_nii_fname volume """ # TODO could make dilation with ndimage also. mask = IOUtils.read_volume(to_label_nii_fname) dil_mask = IOUtils.read_volume(dilated_nii_fname) lab, n = scipy.ndimage.label(dil_mask.data) # TODO: this change is from tvb-make. Keep it or not? It returns a different result than the old version. lab_xyz = list(self.compute_label_volume_centers(lab, dil_mask.affine_matrix)) lab_sort = numpy.r_[:n + 1] # sort labels along AP axis for i, (val, _) in enumerate(sorted(lab_xyz, key=lambda t: t[1][1])): lab_sort[val] = i lab = lab_sort[lab] mask.data *= lab self.logger.info( '%d objects found when labeling the dilated volume.', n) IOUtils.write_volume(out_nii_fname, mask)
def test_label_with_dilation(): service = VolumeService() ct_mask_data = numpy.array([[[0, 0, 0], [0, 1, 0], [0, 1, 0]], [[1, 1, 1], [0, 0, 0], [0, 0, 0]], [[0, 0, 1], [0, 0, 0], [0, 0, 1]]]) ct_mask_volume = Volume( ct_mask_data, [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], None) ct_mask_path = get_temporary_files_path("ct_mask.nii.gz") IOUtils.write_volume(ct_mask_path, ct_mask_volume) ct_dil_mask_data = numpy.array([[[0, 0, 0], [1, 1, 1], [0, 1, 0]], [[1, 1, 1], [0, 0, 0], [0, 0, 0]], [[0, 1, 1], [0, 0, 0], [0, 1, 1]]]) ct_dil_mask_volume = Volume( ct_dil_mask_data, [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], None) ct_dil_mask_path = get_temporary_files_path("ct_dil_mask.nii.gz") IOUtils.write_volume(ct_dil_mask_path, ct_dil_mask_volume) ct_result = get_temporary_files_path("ct_res.nii.gz") service.label_with_dilation(ct_mask_path, ct_dil_mask_path, ct_result) assert os.path.exists(ct_mask_path) assert os.path.exists(ct_dil_mask_path) assert os.path.exists(ct_result) vol = IOUtils.read_volume(ct_result) assert numpy.array_equal(numpy.unique(vol.data), [0, 1, 2, 3])
def test_label_with_dilation(): service = VolumeService() ct_mask_data = numpy.array( [[[0, 0, 0], [0, 1, 0], [0, 1, 0]], [[1, 1, 1], [0, 0, 0], [0, 0, 0]], [[0, 0, 1], [0, 0, 0], [0, 0, 1]]]) ct_mask_volume = Volume(ct_mask_data, [[1, 0, 0, 0], [0, 1, 0, 0], [ 0, 0, 1, 0], [0, 0, 0, 1]], None) ct_mask_path = get_temporary_files_path("ct_mask.nii.gz") IOUtils.write_volume(ct_mask_path, ct_mask_volume) ct_dil_mask_data = numpy.array( [[[0, 0, 0], [1, 1, 1], [0, 1, 0]], [[1, 1, 1], [0, 0, 0], [0, 0, 0]], [[0, 1, 1], [0, 0, 0], [0, 1, 1]]]) ct_dil_mask_volume = Volume(ct_dil_mask_data, [[1, 0, 0, 0], [ 0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], None) ct_dil_mask_path = get_temporary_files_path("ct_dil_mask.nii.gz") IOUtils.write_volume(ct_dil_mask_path, ct_dil_mask_volume) ct_result = get_temporary_files_path("ct_res.nii.gz") service.label_with_dilation(ct_mask_path, ct_dil_mask_path, ct_result) assert os.path.exists(ct_mask_path) assert os.path.exists(ct_dil_mask_path) assert os.path.exists(ct_result) vol = IOUtils.read_volume(ct_result)
def test_write_volume(): in_file_path = get_data_file(TEST_MODIF_SUBJECT, TEST_VOLUME_FOLDER, "T1.nii.gz") volume = IOUtils.read_volume(in_file_path) out_file_path = get_temporary_files_path('T1-out.nii.gz') IOUtils.write_volume(out_file_path, volume) assert os.path.exists(out_file_path)
def test_remove_zero_connectivity(): service = VolumeService() data = numpy.array([[[0, 0, 1], [2, 3, 0]], [[4, 0, 0], [0, 0, 0]]]) volume = Volume(data, [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], None) volume_path = get_temporary_files_path("tdi_lbl.nii.gz") IOUtils.write_volume(volume_path, volume) in_connectivity = numpy.array([[10, 1, 0, 3], [0, 10, 0, 2], [0, 0, 0, 0], [0, 0, 0, 10]]) connectivity_path = get_temporary_files_path("conn.csv") numpy.savetxt(connectivity_path, in_connectivity, fmt='%1d') tract_lengths_path = get_temporary_files_path("tract_lengths.csv") numpy.savetxt(tract_lengths_path, in_connectivity, fmt='%1d') service.remove_zero_connectivity_nodes(volume_path, connectivity_path, tract_lengths_path) assert os.path.exists(os.path.splitext(connectivity_path)[0] + ".npy") assert os.path.exists(os.path.splitext(tract_lengths_path)[0] + ".npy") vol = IOUtils.read_volume(volume_path) assert len(numpy.unique(vol.data)) == 4 conn = numpy.array(numpy.genfromtxt(connectivity_path, dtype='int64')) assert numpy.array_equal(conn, [[20, 1, 3], [1, 20, 2], [3, 2, 20]])
def label_with_dilation(self, to_label_nii_fname: os.PathLike, dilated_nii_fname: os.PathLike, out_nii_fname: os.PathLike): """ Labels a volume using its labeled dilation. The dilated volume is labeled using scipy.ndimage.label function. :param to_label_nii_fname: usually a CT-mask.nii.gz :param dilated_nii_fname: dilated version of the to_label_nii_fname volume """ # TODO could make dilation with ndimage also. mask = IOUtils.read_volume(to_label_nii_fname) dil_mask = IOUtils.read_volume(dilated_nii_fname) lab, n = scipy.ndimage.label(dil_mask.data) # TODO: this change is from tvb-make. Keep it or not? It returns a different result than the old version. lab_xyz = list( self.compute_label_volume_centers(lab, dil_mask.affine_matrix)) lab_sort = numpy.r_[:n + 1] # sort labels along AP axis for i, (val, _) in enumerate(sorted(lab_xyz, key=lambda t: t[1][1])): lab_sort[val] = i lab = lab_sort[lab] mask.data *= lab self.logger.info('%d objects found when labeling the dilated volume.', n) IOUtils.write_volume(out_nii_fname, mask)
def test_remove_zero_connectivity(): service = VolumeService() data = numpy.array([[[0, 0, 1], [2, 3, 0]], [[4, 0, 0], [0, 0, 0]]]) volume = Volume(data, [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], None) volume_path = get_temporary_files_path("tdi_lbl.nii.gz") IOUtils.write_volume(volume_path, volume) in_connectivity = numpy.array( [[10, 1, 0, 3], [0, 10, 0, 2], [0, 0, 0, 0], [0, 0, 0, 10]]) connectivity_path = get_temporary_files_path("conn.csv") numpy.savetxt(connectivity_path, in_connectivity, fmt='%1d') tract_lengths_path = get_temporary_files_path("tract_lengths.csv") numpy.savetxt(tract_lengths_path, in_connectivity, fmt='%1d') service.remove_zero_connectivity_nodes( volume_path, connectivity_path, tract_lengths_path) assert os.path.exists(os.path.splitext(connectivity_path)[0] + ".npy") assert os.path.exists(os.path.splitext(tract_lengths_path)[0] + ".npy") vol = IOUtils.read_volume(volume_path) assert len(numpy.unique(vol.data)) == 4 conn = numpy.array(numpy.genfromtxt(connectivity_path, dtype='int64')) assert numpy.array_equal(conn, [[20, 1, 3], [1, 20, 2], [3, 2, 20]])
def test_write_volume(): in_file_path = get_data_file( TEST_MODIF_SUBJECT, TEST_VOLUME_FOLDER, "T1.nii.gz") volume = IOUtils.read_volume(in_file_path) out_file_path = get_temporary_files_path('T1-out.nii.gz') IOUtils.write_volume(out_file_path, volume) assert os.path.exists(out_file_path)
def simple_label_config(self, in_aparc_path, out_volume_path): """ Relabel volume to have contiguous values like Mrtrix' labelconfig. :param in_aparc_path: volume voxel value is the index of the region it belongs to. :return: writes the labeled volume to out_volume_path. """ aparc = IOUtils.read_volume(in_aparc_path) aparc = self._label_config(aparc) IOUtils.write_volume(out_volume_path, aparc)
def simple_label_config(self, in_aparc_path: os.PathLike, out_volume_path: os.PathLike): """ Relabel volume to have contiguous values like Mrtrix' labelconfig. :param in_aparc_path: volume voxel value is the index of the region it belongs to. :return: writes the labeled volume to out_volume_path. """ aparc = IOUtils.read_volume(in_aparc_path) aparc = self._label_config(aparc) IOUtils.write_volume(out_volume_path, aparc)
def remove_zero_connectivity_nodes( self, node_volume_path: os.PathLike, connectivity_matrix_path: os.PathLike, tract_length_path: Optional[str] = None): """ It removes network nodes with zero connectivity from the volume and connectivity matrices. The zero connectivity nodes will be labeled with 0 in the volume and the remaining labels will be updated. The connectivity matrices will be symmetric. :param node_volume_path: tdi_lbl.nii volume path :param connectivity_matrix_path: .csv file, output of Mrtrix3 tck2connectome :param tract_length_path: optional .csv tract lengths matrix :return: overwrites the input volume and matrices with the processed ones. Also saves matrices as .npy. """ node_volume = IOUtils.read_volume(node_volume_path) connectivity = numpy.array( numpy.genfromtxt(connectivity_matrix_path, dtype='int64')) connectivity = connectivity + connectivity.T connectivity_row_sum = numpy.sum(connectivity, axis=0) nodes_to_keep_indices = connectivity_row_sum > 0 connectivity = connectivity[ nodes_to_keep_indices, :][:, nodes_to_keep_indices] numpy.save( os.path.splitext(connectivity_matrix_path)[0] + NPY_EXTENSION, connectivity) numpy.savetxt(connectivity_matrix_path, connectivity, fmt='%1d') if os.path.exists(str(tract_length_path)): connectivity = numpy.array( numpy.genfromtxt(tract_length_path, dtype='int64')) connectivity = connectivity[ nodes_to_keep_indices, :][:, nodes_to_keep_indices] numpy.save( os.path.splitext(tract_length_path)[0] + NPY_EXTENSION, connectivity) numpy.savetxt(tract_length_path, connectivity, fmt='%1d') else: self.logger.warning("Path %s is not valid.", tract_length_path) nodes_to_remove_indices, = numpy.where(~nodes_to_keep_indices) nodes_to_remove_indices += 1 for node_index in nodes_to_remove_indices: node_volume.data[node_volume.data == node_index] = 0 node_volume.data[node_volume.data > 0] = numpy.r_[1:( connectivity.shape[0] + 1)] IOUtils.write_volume(node_volume_path, node_volume)
def label_vol_from_tdi(self, tdi_volume_path: os.PathLike, out_volume_path: os.PathLike, lo: float=0.5): """ Creates a mask of the voxels with tract ends > lo and any other voxels become 0. Labels each voxel different from 0 with integer labels starting from 1. :param tdi_volume_path: volume voxel value is the sum of tract ends. Voxel without tract ends has value 0. :param lo: tract ends threshold used for masking. :return: writes labeled volume to :ut_volume_path. """ nii_volume = IOUtils.read_volume(tdi_volume_path) tdi_volume = self._label_volume(nii_volume, lo) IOUtils.write_volume(out_volume_path, tdi_volume)
def label_vol_from_tdi(self, tdi_volume_path, out_volume_path, lo=0.5): """ Creates a mask of the voxels with tract ends > lo and any other voxels become 0. Labels each voxel different from 0 with integer labels starting from 1. :param tdi_volume_path: volume voxel value is the sum of tract ends. Voxel without tract ends has value 0. :param lo: tract ends threshold used for masking. :return: writes labeled volume to :ut_volume_path. """ nii_volume = IOUtils.read_volume(tdi_volume_path) tdi_volume = self._label_volume(nii_volume, lo) IOUtils.write_volume(out_volume_path, tdi_volume)
def remove_zero_connectivity_nodes(self, node_volume_path: os.PathLike, connectivity_matrix_path: os.PathLike, tract_length_path: Optional[str]=None): """ It removes network nodes with zero connectivity from the volume and connectivity matrices. The zero connectivity nodes will be labeled with 0 in the volume and the remaining labels will be updated. The connectivity matrices will be symmetric. :param node_volume_path: tdi_lbl.nii volume path :param connectivity_matrix_path: .csv file, output of Mrtrix3 tck2connectome :param tract_length_path: optional .csv tract lengths matrix :return: overwrites the input volume and matrices with the processed ones. Also saves matrices as .npy. """ node_volume = IOUtils.read_volume(node_volume_path) connectivity = numpy.array(numpy.genfromtxt( connectivity_matrix_path, dtype='int64')) connectivity = connectivity + connectivity.T connectivity_row_sum = numpy.sum(connectivity, axis=0) nodes_to_keep_indices = connectivity_row_sum > 0 connectivity = connectivity[nodes_to_keep_indices, :][ :, nodes_to_keep_indices] numpy.save(os.path.splitext(connectivity_matrix_path) [0] + NPY_EXTENSION, connectivity) numpy.savetxt(connectivity_matrix_path, connectivity, fmt='%1d') if os.path.exists(str(tract_length_path)): connectivity = numpy.array(numpy.genfromtxt( tract_length_path, dtype='int64')) connectivity = connectivity[nodes_to_keep_indices, :][ :, nodes_to_keep_indices] numpy.save(os.path.splitext(tract_length_path) [0] + NPY_EXTENSION, connectivity) numpy.savetxt(tract_length_path, connectivity, fmt='%1d') else: self.logger.warning("Path %s is not valid.", tract_length_path) nodes_to_remove_indices, = numpy.where(~nodes_to_keep_indices) nodes_to_remove_indices += 1 for node_index in nodes_to_remove_indices: node_volume.data[node_volume.data == node_index] = 0 node_volume.data[node_volume.data > 0] = numpy.r_[ 1:(connectivity.shape[0] + 1)] IOUtils.write_volume(node_volume_path, node_volume)
def create_tvb_dataset(atlas_suffix: AtlasSuffix, mri_direc: os.PathLike, region_details_direc: os.PathLike, weights_file: os.PathLike, tracts_file: os.PathLike, out_dir: os.PathLike, bring_t1=False): weights_matrix = numpy.loadtxt(str(weights_file), dtype='i', delimiter=' ') weights_matrix += weights_matrix.T tracts_matrix = numpy.loadtxt(str(tracts_file), dtype='f', delimiter=' ') tracts_matrix += tracts_matrix.T is_cortical_rm = numpy.genfromtxt( os.path.join(region_details_direc, AsegFiles.CORTICAL_TXT.value.replace("%s", atlas_suffix)), usecols=[0], dtype='i') region_names = numpy.genfromtxt( os.path.join(region_details_direc, AsegFiles.CENTERS_TXT.value.replace("%s", atlas_suffix)), usecols=[0], dtype="str") region_centers = numpy.genfromtxt( os.path.join(region_details_direc, AsegFiles.CENTERS_TXT.value.replace("%s", atlas_suffix)), usecols=[1, 2, 3]) region_areas = numpy.genfromtxt( os.path.join(region_details_direc, AsegFiles.AREAS_TXT.value.replace("%s", atlas_suffix)), usecols=[0]) region_orientations = numpy.genfromtxt( os.path.join(region_details_direc, AsegFiles.ORIENTATIONS_TXT.value.replace("%s", atlas_suffix)), usecols=[0, 1, 2]) rm_idx = numpy.genfromtxt( os.path.join(region_details_direc, AsegFiles.RM_TO_APARC_ASEG_TXT.value.replace("%s", atlas_suffix)), usecols=[0, 1], dtype='i') rm_index_dict = dict(zip(rm_idx[:, 0], rm_idx[:, 1])) print(rm_index_dict) genericIO = GenericIO() genericIO.write_connectivity_zip(out_dir, weights_matrix, tracts_matrix, is_cortical_rm, region_names, region_centers, region_areas, region_orientations, atlas_suffix) aparc_aseg_file = os.path.join(mri_direc, T1Files.APARC_ASEG_NII_GZ.value.replace("%s", atlas_suffix)) aparc_aseg_volume = IOUtils.read_volume(aparc_aseg_file) volume_service = VolumeService() aparc_aseg_cor_volume = volume_service.change_labels_of_aparc_aseg(atlas_suffix, aparc_aseg_volume, rm_index_dict, weights_matrix.shape[0]) IOUtils.write_volume(os.path.join(out_dir, OutputConvFiles.APARC_ASEG_COR_NII_GZ.value.replace("%s", atlas_suffix)), aparc_aseg_cor_volume) if bring_t1: shutil.copy2(os.path.join(mri_direc, "T1.nii.gz"), out_dir)
def create_tvb_dataset(atlas_suffix: AtlasSuffix, mri_direc: os.PathLike, region_details_direc: os.PathLike, weights_file: os.PathLike, tracts_file: os.PathLike, out_dir: os.PathLike, bring_t1=False): weights_matrix = numpy.loadtxt(str(weights_file), dtype='i', delimiter=',') weights_matrix += weights_matrix.T tracts_matrix = numpy.loadtxt(str(tracts_file), dtype='f', delimiter=',') tracts_matrix += tracts_matrix.T is_cortical_rm = numpy.genfromtxt( os.path.join(region_details_direc, AsegFiles.CORTICAL_TXT.value.replace("%s", atlas_suffix)), usecols=[0], dtype='i') region_names = numpy.genfromtxt( os.path.join(region_details_direc, AsegFiles.CENTERS_TXT.value.replace("%s", atlas_suffix)), usecols=[0], dtype="str") region_centers = numpy.genfromtxt( os.path.join(region_details_direc, AsegFiles.CENTERS_TXT.value.replace("%s", atlas_suffix)), usecols=[1, 2, 3]) region_areas = numpy.genfromtxt( os.path.join(region_details_direc, AsegFiles.AREAS_TXT.value.replace("%s", atlas_suffix)), usecols=[0]) region_orientations = numpy.genfromtxt( os.path.join(region_details_direc, AsegFiles.ORIENTATIONS_TXT.value.replace("%s", atlas_suffix)), usecols=[0, 1, 2]) rm_idx = numpy.genfromtxt( os.path.join(region_details_direc, AsegFiles.RM_TO_APARC_ASEG_TXT.value.replace("%s", atlas_suffix)), usecols=[0, 1], dtype='i') rm_index_dict = dict(zip(rm_idx[:, 0], rm_idx[:, 1])) print(rm_index_dict) genericIO = GenericIO() genericIO.write_connectivity_zip(out_dir, weights_matrix, tracts_matrix, is_cortical_rm, region_names, region_centers, region_areas, region_orientations, atlas_suffix) aparc_aseg_file = os.path.join(mri_direc, T1Files.APARC_ASEG_NII_GZ.value.replace("%s", atlas_suffix)) aparc_aseg_volume = IOUtils.read_volume(aparc_aseg_file) volume_service = VolumeService() aparc_aseg_cor_volume = volume_service.change_labels_of_aparc_aseg(atlas_suffix, aparc_aseg_volume, rm_index_dict, weights_matrix.shape[0]) IOUtils.write_volume(os.path.join(out_dir, OutputConvFiles.APARC_ASEG_COR_NII_GZ.value.replace("%s", atlas_suffix)), aparc_aseg_cor_volume) if bring_t1: shutil.copy2(os.path.join(mri_direc, "T1.nii.gz"), out_dir)
def label_with_dilation(self, to_label_nii_fname, dilated_nii_fname, out_nii_fname): """ Labels a volume using its labeled dilation. The dilated volume is labeled using scipy.ndimage.label function. :param to_label_nii_fname: usually a CT-mask.nii.gz :param dilated_nii_fname: dilated version of the to_label_nii_fname volume """ # TODO could make dilation with ndimage also. mask = IOUtils.read_volume(to_label_nii_fname) dil_mask = IOUtils.read_volume(dilated_nii_fname) lab, n = scipy.ndimage.label(dil_mask.data) mask.data *= lab self.logger.info('%d objects found when labeling the dilated volume.', n) IOUtils.write_volume(out_nii_fname, mask)
def mask_to_vol(self, in_vol_path, mask_vol_path, out_vol_path=None, labels=None, ctx=None, vol2mask_path=None, vn=1, th=0.999, labels_mask=None, labels_nomask='0'): """ Identify the voxels that are neighbors within a voxel distance vn, to a mask volume, with a mask threshold of th Default behavior: we assume a binarized mask and set th=0.999, no neighbors search, only looking at the exact voxel position, i.e., vn=0. Accepted voxels retain their label, whereas rejected ones get a label of 0 """ # Set the target labels: labels = self.annotation_service.read_input_labels(labels=labels, ctx=ctx) number_of_labels = len(labels) # Set the labels for the selected voxels if labels_mask is None: labels_mask = labels else: # Read the labels and make sure there is one for each label labels_mask = numpy.array(labels_mask.split()).astype('i') if len(labels_mask) == 1: labels_mask = numpy.repeat(labels_mask, number_of_labels).tolist() elif len(labels_mask) != number_of_labels: self.logger.warning( "Output labels for selected voxels are neither of length 1 nor of length equal to " "the one of target labels") return else: labels_mask = labels_mask.tolist() # Read the excluded labels and make sure there is one for each label labels_nomask = numpy.array(labels_nomask.split()).astype('i') if len(labels_nomask) == 1: labels_nomask = numpy.repeat(labels_nomask, number_of_labels).tolist() elif len(labels_nomask) != number_of_labels: self.logger.warning( "Output labels for excluded voxels are neither of length 1 nor of length equal to the " "one of the target labels") return else: labels_nomask = labels_nomask.tolist() volume = IOUtils.read_volume(in_vol_path) mask_vol = IOUtils.read_volume(mask_vol_path) # Compute the transform from vol ijk to mask ijk: ijk2ijk = numpy.identity(4) # If vol and mask are not in the same space: if os.path.exists(str(vol2mask_path)): # read the xyz2xyz transform and apply it to the inverse mask # affine transform to get an ijk2ijk transform. xyz2xyz = numpy.loadtxt(vol2mask_path) ijk2ijk = volume.affine_matrix.dot( numpy.dot(xyz2xyz, numpy.linalg.inv(mask_vol.affine_matrix))) # Construct a grid template of voxels +/- vn voxels around each ijk # voxel, sharing at least a corner grid = numpy.meshgrid(list(range(-vn, vn + 1, 1)), list(range(-vn, vn + 1, 1)), list(range(-vn, vn + 1, 1)), indexing='ij') grid = numpy.c_[numpy.array(grid[0]).flatten(), numpy.array(grid[1]).flatten(), numpy.array(grid[2]).flatten()] n_grid = grid.shape[0] out_volume = Volume(numpy.array(volume.data), volume.affine_matrix, volume.header) # Initialize output indexes out_ijk = [] # For each target label: for label_index in range(number_of_labels): current_label = labels[label_index] # Get the indexes of all voxels of this label: label_voxels_i, label_voxels_j, label_voxels_k = numpy.where( volume.data == current_label) for voxel_index in range(label_voxels_i.size): current_voxel_i, current_voxel_j, current_voxel_k = \ label_voxels_i[voxel_index], label_voxels_j[ voxel_index], label_voxels_k[voxel_index] # TODO if necessary: deal with voxels at the edge of the image, such as brain stem ones... # if any([(i==0), (i==mask_shape[0]-1),(j==0), (j==mask_shape[0]-1),(k==0), (k==mask_shape[0]-1)]): # mask_shape[i,j,k]=0 # continue # ...get the corresponding voxel in the mask volume: ijk = numpy.round( ijk2ijk.dot( numpy.array([ current_voxel_i, current_voxel_j, current_voxel_k, 1 ]))[:3]).astype('i') # Make sure this point is within image limits for cc in range(3): if ijk[cc] < 0: ijk[cc] = 0 elif ijk[cc] >= mask_vol.dimensions[cc]: ijk[cc] = mask_vol.dimensions[cc] - 1 # If this is a voxel to keep, set it so... if mask_vol.data[ijk[0], ijk[1], ijk[2]] >= th: out_volume.data[current_voxel_i, current_voxel_j, current_voxel_k] = labels_mask[label_index] out_ijk.append( [current_voxel_i, current_voxel_j, current_voxel_k]) elif vn > 0: # If not, and as long as vn>0 check whether any of its vn neighbors is a mask voxel. # Generate the specific grid centered at the vertex ijk ijk_grid = grid + numpy.tile(ijk, (n_grid, 1)) # Remove voxels outside the mask volume indexes_within_limits = numpy.all( [(ijk_grid[:, 0] >= 0), (ijk_grid[:, 0] < mask_vol.dimensions[0]), (ijk_grid[:, 1] >= 0), (ijk_grid[:, 1] < mask_vol.dimensions[1]), (ijk_grid[:, 2] >= 0), (ijk_grid[:, 2] < mask_vol.dimensions[2])], axis=0) ijk_grid = ijk_grid[indexes_within_limits, :] try: # If none of these points is a mask point: if (mask_vol.data[ijk_grid[:, 0], ijk_grid[:, 1], ijk_grid[:, 2]] < th).all(): out_volume.data[ current_voxel_i, current_voxel_j, current_voxel_k] = labels_nomask[label_index] else: # if any of them is a mask point: out_volume.data[ current_voxel_i, current_voxel_j, current_voxel_k] = labels_mask[label_index] out_ijk.append([ current_voxel_i, current_voxel_j, current_voxel_k ]) except ValueError: # empty grid self.logger.error( "Error at voxel ( %s, %s, %s ): It appears to have no common-face neighbors " "inside the image!", str(current_voxel_i), str(current_voxel_j), str(current_voxel_k)) return else: out_volume.data[ current_voxel_i, current_voxel_j, current_voxel_k] = labels_nomask[label_index] if out_vol_path is None: out_vol_path = in_vol_path IOUtils.write_volume(out_vol_path, out_volume) # Save the output indexes that survived masking out_ijk = numpy.vstack(out_ijk) filepath = os.path.splitext(out_vol_path)[0] numpy.save(filepath + "-idx.npy", out_ijk) numpy.savetxt(filepath + "-idx.txt", out_ijk, fmt='%d')
def vol_to_ext_surf_vol(self, in_vol_path, labels=None, ctx=None, out_vol_path=None, labels_surf=None, labels_inner='0'): """ Separate the voxels of the outer surface of a structure, from the inner ones. Default behavior: surface voxels retain their label, inner voxels get the label 0, and the input file is overwritten by the output. """ labels = self.annotation_service.read_input_labels(labels=labels, ctx=ctx) number_of_labels = len(labels) # Set the labels for the surfaces if labels_surf is None: labels_surf = labels else: # Read the surface labels and make sure there is one for each label labels_surf = numpy.array(labels_surf.split()).astype('i') if len(labels_surf) == 1: labels_surf = numpy.repeat(labels_inner, number_of_labels).tolist() elif len(labels_surf) != number_of_labels: self.logger.warning( "Output labels for surface voxels are neither of length " "1 nor of length equal to the one of target labels.") return else: labels_surf = labels_surf.tolist() # Read the inner, non-surface labels labels_inner = numpy.array(labels_inner.split()).astype('i') # ...and make sure there is one for each label if len(labels_inner) == 1: labels_inner = numpy.repeat(labels_inner, number_of_labels).tolist() elif len(labels_inner) != number_of_labels: self.logger.warning( "Output labels for inner voxels are neither of length 1 nor " "of length equal to the one of the target labels.") return else: labels_inner = labels_inner.tolist() # Read the input volume... volume = IOUtils.read_volume(in_vol_path) # Neigbors' grid sharing a face eye3 = numpy.identity(3) border_grid = numpy.c_[eye3, -eye3].T.astype('i') n_border = 6 out_volume = Volume(numpy.array(volume.data), volume.affine_matrix, volume.header) # Initialize output indexes out_ijk = [] for label_index in range(number_of_labels): current_label = labels[label_index] # Get the indexes of all voxels of this label: label_volxels_i, label_voxels_j, label_voxels_k = numpy.where( volume.data == current_label) # and for each voxel for voxel_index in range(label_volxels_i.size): # indexes of this voxel: current_voxel_i, current_voxel_j, current_voxel_k = \ label_volxels_i[voxel_index], label_voxels_j[ voxel_index], label_voxels_k[voxel_index] # Create the neighbors' grid sharing a face ijk_grid = border_grid + \ numpy.tile(numpy.array( [current_voxel_i, current_voxel_j, current_voxel_k]), (n_border, 1)) # Remove voxels outside the image indices_inside_image = numpy.all( [(ijk_grid[:, 0] >= 0), (ijk_grid[:, 0] < volume.dimensions[0]), (ijk_grid[:, 1] >= 0), (ijk_grid[:, 1] < volume.dimensions[1]), (ijk_grid[:, 2] >= 0), (ijk_grid[:, 2] < volume.dimensions[2])], axis=0) ijk_grid = ijk_grid[indices_inside_image, :] try: # If all face neighbors are of the same label... if numpy.all( volume.data[ijk_grid[:, 0], ijk_grid[:, 1], ijk_grid[:, 2]] == numpy.tile( volume.data[current_voxel_i, current_voxel_j, current_voxel_k], ( n_border, 1))): # ...set this voxel to the corresponding inner target label out_volume.data[ current_voxel_i, current_voxel_j, current_voxel_k] = labels_inner[label_index] else: # ...set this voxel to the corresponding surface target label out_volume.data[ current_voxel_i, current_voxel_j, current_voxel_k] = labels_surf[label_index] out_ijk.append([ current_voxel_i, current_voxel_j, current_voxel_k ]) except ValueError: # empty grid self.logger.error( "Error at voxel ( %s, %s, %s ) of label %s: It appears to have no common-face " "neighbors inside the image!", str(current_voxel_i), str(current_voxel_j), str(current_voxel_k), str(current_label)) return if out_vol_path is None: out_vol_path = in_vol_path IOUtils.write_volume(out_vol_path, out_volume) # save the output indexes that survived masking out_ijk = numpy.vstack(out_ijk) filepath = os.path.splitext(out_vol_path)[0] numpy.save(filepath + "-idx.npy", out_ijk) numpy.savetxt(filepath + "-idx.txt", out_ijk, fmt='%d')
def vol_to_ext_surf_vol(self, in_vol_path: os.PathLike, labels: Optional[Union[numpy.ndarray, list]]=None, ctx: Optional[os.PathLike]=None, out_vol_path: Optional[os.PathLike]=None, labels_surf: Optional[Union[numpy.ndarray, list]]=None, labels_inner: str='0'): """ Separate the voxels of the outer surface of a structure, from the inner ones. Default behavior: surface voxels retain their label, inner voxels get the label 0, and the input file is overwritten by the output. """ labels = self.annotation_service.read_input_labels( labels=labels, ctx=ctx) number_of_labels = len(labels) # Set the labels for the surfaces if labels_surf is None: labels_surf = labels else: # Read the surface labels and make sure there is one for each label labels_surf = numpy.array(labels_surf.split()).astype('i') if len(labels_surf) == 1: labels_surf = numpy.repeat( labels_inner, number_of_labels).tolist() elif len(labels_surf) != number_of_labels: self.logger.warning( "Output labels for surface voxels are neither of length " "1 nor of length equal to the one of target labels.") return else: labels_surf = labels_surf.tolist() # Read the inner, non-surface labels labels_inner = numpy.array(labels_inner.split()).astype('i') # ...and make sure there is one for each label if len(labels_inner) == 1: labels_inner = numpy.repeat( labels_inner, number_of_labels).tolist() elif len(labels_inner) != number_of_labels: self.logger.warning( "Output labels for inner voxels are neither of length 1 nor " "of length equal to the one of the target labels.") return else: labels_inner = labels_inner.tolist() # Read the input volume... volume = IOUtils.read_volume(in_vol_path) # Neigbors' grid sharing a face eye3 = numpy.identity(3) border_grid = numpy.c_[eye3, -eye3].T.astype('i') n_border = 6 out_volume = Volume(numpy.array(volume.data), volume.affine_matrix, volume.header) # Initialize output indexes out_ijk = [] for label_index in range(number_of_labels): current_label = labels[label_index] # Get the indexes of all voxels of this label: label_volxels_i, label_voxels_j, label_voxels_k = numpy.where( volume.data == current_label) # and for each voxel for voxel_index in range(label_volxels_i.size): # indexes of this voxel: current_voxel_i, current_voxel_j, current_voxel_k = \ label_volxels_i[voxel_index], label_voxels_j[ voxel_index], label_voxels_k[voxel_index] # Create the neighbors' grid sharing a face ijk_grid = border_grid + \ numpy.tile(numpy.array( [current_voxel_i, current_voxel_j, current_voxel_k]), (n_border, 1)) # Remove voxels outside the image indices_inside_image = numpy.all([(ijk_grid[:, 0] >= 0), (ijk_grid[:, 0] < volume.dimensions[0]), (ijk_grid[:, 1] >= 0), (ijk_grid[ :, 1] < volume.dimensions[1]), (ijk_grid[:, 2] >= 0), (ijk_grid[:, 2] < volume.dimensions[2])], axis=0) ijk_grid = ijk_grid[indices_inside_image, :] try: # If all face neighbors are of the same label... if numpy.all(volume.data[ijk_grid[:, 0], ijk_grid[:, 1], ijk_grid[:, 2]] == numpy.tile( volume.data[current_voxel_i, current_voxel_j, current_voxel_k], (n_border, 1))): # ...set this voxel to the corresponding inner target label out_volume.data[current_voxel_i, current_voxel_j, current_voxel_k] = labels_inner[label_index] else: # ...set this voxel to the corresponding surface target label out_volume.data[current_voxel_i, current_voxel_j, current_voxel_k] = labels_surf[label_index] out_ijk.append( [current_voxel_i, current_voxel_j, current_voxel_k]) except ValueError: # empty grid self.logger.error("Error at voxel ( %s, %s, %s ) of label %s: It appears to have no common-face " "neighbors inside the image!", str( current_voxel_i), str(current_voxel_j), str(current_voxel_k), str(current_label)) return if out_vol_path is None: out_vol_path = in_vol_path IOUtils.write_volume(out_vol_path, out_volume) # save the output indexes that survived masking out_ijk = numpy.vstack(out_ijk) filepath = os.path.splitext(out_vol_path)[0] numpy.save(filepath + "-idx.npy", out_ijk) numpy.savetxt(filepath + "-idx.txt", out_ijk, fmt='%d')
def mask_to_vol(self, in_vol_path: os.PathLike, mask_vol_path: os.PathLike, out_vol_path: Optional[os.PathLike]=None, labels: Optional[Union[numpy.ndarray, list]]=None, ctx: Optional[str]=None, vol2mask_path: Optional[os.PathLike]=None, vn: int=1, th: float=0.999, labels_mask: Optional[os.PathLike]=None, labels_nomask: str='0'): """ Identify the voxels that are neighbors within a voxel distance vn, to a mask volume, with a mask threshold of th Default behavior: we assume a binarized mask and set th=0.999, no neighbors search, only looking at the exact voxel position, i.e., vn=0. Accepted voxels retain their label, whereas rejected ones get a label of 0 """ # Set the target labels: labels = self.annotation_service.read_input_labels( labels=labels, ctx=ctx) number_of_labels = len(labels) # Set the labels for the selected voxels if labels_mask is None: labels_mask = labels else: # Read the labels and make sure there is one for each label labels_mask = numpy.array(labels_mask.split()).astype('i') if len(labels_mask) == 1: labels_mask = numpy.repeat( labels_mask, number_of_labels).tolist() elif len(labels_mask) != number_of_labels: self.logger.warning("Output labels for selected voxels are neither of length 1 nor of length equal to " "the one of target labels") return else: labels_mask = labels_mask.tolist() # Read the excluded labels and make sure there is one for each label labels_nomask = numpy.array(labels_nomask.split()).astype('i') if len(labels_nomask) == 1: labels_nomask = numpy.repeat( labels_nomask, number_of_labels).tolist() elif len(labels_nomask) != number_of_labels: self.logger.warning("Output labels for excluded voxels are neither of length 1 nor of length equal to the " "one of the target labels") return else: labels_nomask = labels_nomask.tolist() volume = IOUtils.read_volume(in_vol_path) mask_vol = IOUtils.read_volume(mask_vol_path) # Compute the transform from vol ijk to mask ijk: ijk2ijk = numpy.identity(4) # If vol and mask are not in the same space: if os.path.exists(str(vol2mask_path)): # read the xyz2xyz transform and apply it to the inverse mask # affine transform to get an ijk2ijk transform. xyz2xyz = numpy.loadtxt(vol2mask_path) ijk2ijk = volume.affine_matrix.dot( numpy.dot(xyz2xyz, numpy.linalg.inv(mask_vol.affine_matrix))) # Construct a grid template of voxels +/- vn voxels around each ijk # voxel, sharing at least a corner grid = numpy.meshgrid(list(range(-vn, vn + 1, 1)), list( range(-vn, vn + 1, 1)), list(range(-vn, vn + 1, 1)), indexing='ij') grid = numpy.c_[numpy.array(grid[0]).flatten(), numpy.array( grid[1]).flatten(), numpy.array(grid[2]).flatten()] n_grid = grid.shape[0] out_volume = Volume(numpy.array(volume.data), volume.affine_matrix, volume.header) # Initialize output indexes out_ijk = [] # For each target label: for label_index in range(number_of_labels): current_label = labels[label_index] # Get the indexes of all voxels of this label: label_voxels_i, label_voxels_j, label_voxels_k = numpy.where( volume.data == current_label) for voxel_index in range(label_voxels_i.size): current_voxel_i, current_voxel_j, current_voxel_k = \ label_voxels_i[voxel_index], label_voxels_j[ voxel_index], label_voxels_k[voxel_index] # TODO if necessary: deal with voxels at the edge of the image, such as brain stem ones... # if any([(i==0), (i==mask_shape[0]-1),(j==0), (j==mask_shape[0]-1),(k==0), (k==mask_shape[0]-1)]): # mask_shape[i,j,k]=0 # continue # ...get the corresponding voxel in the mask volume: ijk = numpy.round(ijk2ijk.dot(numpy.array( [current_voxel_i, current_voxel_j, current_voxel_k, 1]))[:3]).astype('i') # Make sure this point is within image limits for cc in range(3): if ijk[cc] < 0: ijk[cc] = 0 elif ijk[cc] >= mask_vol.dimensions[cc]: ijk[cc] = mask_vol.dimensions[cc] - 1 # If this is a voxel to keep, set it so... if mask_vol.data[ijk[0], ijk[1], ijk[2]] >= th: out_volume.data[current_voxel_i, current_voxel_j, current_voxel_k] = labels_mask[label_index] out_ijk.append( [current_voxel_i, current_voxel_j, current_voxel_k]) elif vn > 0: # If not, and as long as vn>0 check whether any of its vn neighbors is a mask voxel. # Generate the specific grid centered at the vertex ijk ijk_grid = grid + numpy.tile(ijk, (n_grid, 1)) # Remove voxels outside the mask volume indexes_within_limits = numpy.all([(ijk_grid[:, 0] >= 0), (ijk_grid[:, 0] < mask_vol.dimensions[0]), (ijk_grid[:, 1] >= 0), (ijk_grid[ :, 1] < mask_vol.dimensions[1]), (ijk_grid[:, 2] >= 0), (ijk_grid[:, 2] < mask_vol.dimensions[2])], axis=0) ijk_grid = ijk_grid[indexes_within_limits, :] try: # If none of these points is a mask point: if (mask_vol.data[ijk_grid[:, 0], ijk_grid[ :, 1], ijk_grid[:, 2]] < th).all(): out_volume.data[ current_voxel_i, current_voxel_j, current_voxel_k] = labels_nomask[label_index] else: # if any of them is a mask point: out_volume.data[ current_voxel_i, current_voxel_j, current_voxel_k] = labels_mask[label_index] out_ijk.append( [current_voxel_i, current_voxel_j, current_voxel_k]) except ValueError: # empty grid self.logger.error("Error at voxel ( %s, %s, %s ): It appears to have no common-face neighbors " "inside the image!", str( current_voxel_i), str(current_voxel_j), str(current_voxel_k)) return else: out_volume.data[current_voxel_i, current_voxel_j, current_voxel_k] = labels_nomask[label_index] if out_vol_path is None: out_vol_path = in_vol_path IOUtils.write_volume(out_vol_path, out_volume) # Save the output indexes that survived masking out_ijk = numpy.vstack(out_ijk) filepath = os.path.splitext(out_vol_path)[0] numpy.save(filepath + "-idx.npy", out_ijk) numpy.savetxt(filepath + "-idx.txt", out_ijk, fmt='%d')