def create_mask_from_contours(self,
img_obj,
draw_method="ray_cast",
disconnected_segments="keep_as_is",
settings=None):
# Creates an image based on provided contours
# Skip if image object is empty
if img_obj.is_missing:
self.roi = None
return
if settings is not None:
disconnected_segments = settings.general.divide_disconnected_roi
# Create an empty roi volume
roi_mask = np.zeros(img_obj.size, dtype=np.bool)
# Iterate over contours to fill out the mask
for contour in self.contour:
# Multiple methods are implemented. All methods return a slice_list (containing slice numbers (z)) and a mask list, which contain boolean masks for respective
# slices. This are then inserted at the specified slice positions, using an OR operation. This operation is required to avoid overwriting different slices.
# Ray casting method to draw segmentation map based on polygon contour
if draw_method == "ray_cast":
slice_list, mask_list = contour.contour_to_grid_ray_cast(
img_obj=img_obj)
for ii in np.arange(len(slice_list)):
slice_id = slice_list[ii]
roi_mask[slice_id, :, :] = np.logical_or(
roi_mask[slice_id, :, :], mask_list[ii])
if disconnected_segments == "keep_largest":
# Check if the created roi mask consists of multiple, separate segments, and keep only the largest.
import skimage.measure
# Label regions
roi_label_mask, n_regions = skimage.measure.label(input=roi_mask,
connectivity=2,
return_num=True)
# Determine size of regions
roi_sizes = np.zeros(n_regions)
for ii in np.arange(start=0, stop=n_regions):
roi_sizes[ii] = np.sum(roi_label_mask == ii + 1)
# Select largest region
roi_mask = roi_label_mask == np.argmax(roi_sizes) + 1
# Store roi as image object
self.roi = ImageClass(voxel_grid=roi_mask,
origin=img_obj.origin,
spacing=img_obj.spacing,
orientation=img_obj.orientation,
slice_z_pos=img_obj.slice_z_pos)
# Remove contour information
self.contour = None
def read_itk_image(image_folder, modality=None, name_contains=None):
# Identify the ITK (NIfTI or NRRD) file.
itk_file = _find_itk_images(image_folder=image_folder,
name_contains=name_contains,
is_mask=False)
# Load the image
sitk_img = sitk.ReadImage(os.path.join(image_folder, itk_file))
# Import the image volume
voxel_grid = sitk.GetArrayFromImage(sitk_img)
# Determine origin, spacing, and orientation
image_origin = np.array(sitk_img.GetOrigin())[::-1]
image_spacing = np.array(sitk_img.GetSpacing())[::-1]
image_orientation = np.array(sitk_img.GetDirection())[::-1]
# Determine z-positions of the slices
slice_z_pos = image_origin[0] + np.arange(
voxel_grid.shape[0]) * image_spacing[0]
# Create an ImageClass object from the input image.
image_obj = ImageClass(voxel_grid=voxel_grid,
origin=image_origin,
spacing=image_spacing,
slice_z_pos=slice_z_pos,
orientation=image_orientation,
modality=modality,
spat_transform="base",
no_image=False)
return image_obj
def merge_roi_objects(roi_list):
"""
Combine multiple roi objects into one
:param roi_list:
:return:
"""
# If there are no rois to combine, return the only roi object
if len(roi_list) == 1:
return roi_list[0]
# Read basic information concerning the roi mask
roi_origin = roi_list[0].roi.origin
roi_spacing = roi_list[0].roi.spacing
roi_orientation = roi_list[0].roi.orientation
roi_size = roi_list[0].roi.size
roi_mask = np.zeros(roi_size, dtype=np.bool)
# Iterate over rois and perform checks
for roi in roi_list:
# Ensure that voxel masks have been created
if roi.contour is not None:
raise ValueError("ROI needs to exist as a mask prior to merging.")
# Ensure that the origin is the same
if not np.all(np.equal(roi_origin, roi.roi.origin)):
raise ValueError("Merged ROIs have mismatching origins.")
# Ensure that spacing is the same
if not np.all(np.equal(roi_spacing, roi.roi.spacing)):
raise ValueError("Merged ROIs have mismatching voxel spacing.")
# Ensure that orientation is the same
if not np.all(np.equal(roi_orientation, roi.roi.orientation)):
raise ValueError("Merged ROIs have mismatching orientations.")
# Ensure that size is the same
if not np.all(np.equal(roi_size, roi.roi.size)):
raise ValueError("Merged ROIs do not have the same size.")
roi_mask = np.logical_or(roi_mask, roi.roi.get_voxel_grid())
# Create a roi mask object
roi_mask_obj = ImageClass(voxel_grid=roi_mask,
origin=roi_origin,
spacing=roi_spacing,
orientation=roi_orientation)
# Set name of the roi
combined_roi_name = "+".join([roi.name for roi in roi_list])
# Create a merged roi object
combined_roi = RoiClass(name=combined_roi_name,
contour=None,
roi_mask=roi_mask_obj)
return combined_roi
def _load_itk_segmentation(image_folder, roi: str):
# Deparse roi
deparsed_roi = parse_roi_name(roi=roi)
# Create an empty roi_list. This list will be iteratively expanded.
roi_list = []
for single_roi in deparsed_roi:
# Not every roi may be found as a file, e.g. in a shotgun approach. We capture
# NoITKSegmentationFileFound errors that only occur if the segmentation cannot be found.
try:
file_name = _find_itk_images(image_folder=image_folder,
name_contains=single_roi,
is_mask=True)
except NoITKSegmentationFileFound:
continue
# Load the segmentation file
sitk_img = sitk.ReadImage(os.path.join(image_folder, file_name))
# Obtain mask
mask = sitk.GetArrayFromImage(sitk_img).astype(np.bool)
# Determine origin, spacing, and orientation
mask_origin = np.array(sitk_img.GetOrigin())[::-1]
mask_spacing = np.array(sitk_img.GetSpacing())[::-1]
mask_orientation = np.array(sitk_img.GetDirection())[::-1]
# Determine z-positions of the slices
slice_z_pos = mask_origin[0] + np.arange(
mask.shape[0]) * mask_spacing[0]
# Create an ImageClass object using the mask.
roi_mask_obj = ImageClass(voxel_grid=mask,
origin=mask_origin,
spacing=mask_spacing,
slice_z_pos=slice_z_pos,
orientation=mask_orientation,
modality="SEG",
spat_transform="base",
no_image=False)
roi_list += [
RoiClass(name=single_roi, contour=None, roi_mask=roi_mask_obj)
]
# Attempt to merge deparsed roi objects.
if len(roi_list) > 0:
roi_obj = merge_roi_objects(roi_list=roi_list)
else:
roi_obj = None
return roi_obj
class RoiClass:
# Class for regions of interest
def __init__(self, name, contour, roi_mask=None, g_range=np.array([np.nan, np.nan]), incl_threshold=0.5, metadata=None):
self.name = name
if contour is not None:
self.contour = contour
else:
self.contour = None
# Fixed parameters
self.g_range = g_range # Range of allowed grey level intensities
self.incl_threshold = incl_threshold # Threshold for partial volume effect
self.adapt_size = 0.0 # Shrinkage and growth of roi
self.svx_randomisation_id = -1 # Randomisation id for supervoxel roi randomisation
# ROI masks
self.roi: Union[ImageClass, None] = roi_mask # Union of intensity and morphology masks
self.roi_intensity: Union[ImageClass, None] = None # Intensity mask of the ROI
self.roi_morphology: Union[ImageClass, None] = None # Morphological mask of the ROI
# Diagnostics features
self.diagnostic_list = []
self.metadata: FileDataset = metadata
def copy(self, drop_image=False):
roi_copy = copy.deepcopy(self)
if drop_image:
roi_copy.roi.drop_image()
if roi_copy.roi_intensity is not None:
roi_copy.roi_intensity.drop_image()
if roi_copy.roi_morphology is not None:
roi_copy.roi_morphology.drop_image()
# Creates a new copy of the roi
return roi_copy
def create_mask_from_contours(self, img_obj, draw_method="ray_cast", disconnected_segments="keep_as_is", settings=None):
# Creates an image based on provided contours
# Skip if image object is empty
if img_obj.is_missing:
self.roi = None
return
if settings is not None:
disconnected_segments = settings.general.divide_disconnected_roi
# Create an empty roi volume
roi_mask = np.zeros(img_obj.size, dtype=np.bool)
# Create empty slice and mask lists.
slice_list = []
mask_list = []
# Iterate over contours to fill out the mask
for contour in self.contour:
# Multiple methods are implemented. All methods return a slice_list (containing slice numbers (z)) and a mask list, which contain boolean masks for respective
# slices. This are then inserted at the specified slice positions, using an OR operation. This operation is required to avoid overwriting different slices.
# Ray casting method to draw segmentation map based on polygon contour
if draw_method == "ray_cast":
contour_slice_list, contour_mask_list = contour.contour_to_grid_ray_cast(img_obj=img_obj)
slice_list += contour_slice_list
mask_list += contour_mask_list
# Check for out-of-range slices.
if len(slice_list) > 0:
# Identify if there any both negative or positive values in slice_list.
if any([slice_id < 0 for slice_id in slice_list]) and not all([slice_id < 0 for slice_id in slice_list]):
mask_list = [mask_list[ii] for ii, unused in enumerate(mask_list) if slice_list[ii] >= 0]
slice_list = [slice_id for slice_id in slice_list if slice_id >= 0]
# Identify any slices that lie outside the negative or positive z-range.
mask_list = [mask_list[ii] for ii, unused in enumerate(mask_list) if abs(slice_list[ii]) < img_obj.size[0]]
slice_list = [slice_id for slice_id in slice_list if slice_id < img_obj.size[0]]
# Set mask.
if len(slice_list) > 0:
# Iterate over the elements in the slice list.
for ii in np.arange(len(slice_list)):
slice_id = slice_list[ii]
roi_mask[slice_id, :, :] = np.logical_or(roi_mask[slice_id, :, :], mask_list[ii])
if disconnected_segments == "keep_largest":
# Check if the created roi mask consists of multiple, separate segments, and keep only the largest.
import skimage.measure
# Label regions
roi_label_mask, n_regions = skimage.measure.label(input=roi_mask, connectivity=2, return_num=True)
# Determine size of regions
roi_sizes = np.zeros(n_regions)
for ii in np.arange(start=0, stop=n_regions):
roi_sizes[ii] = np.sum(roi_label_mask == ii + 1)
# Select largest region
roi_mask = roi_label_mask == np.argmax(roi_sizes) + 1
# Store roi as image object
self.roi = ImageClass(voxel_grid=roi_mask, origin=img_obj.origin, spacing=img_obj.spacing, orientation=img_obj.orientation)
# Remove contour information
self.contour = None
def decimate(self, by_slice):
"""
Decimates the roi
:param by_slice: boolean, 2D (True) or 3D (False)
:return:
"""
# Resect masks
if self.roi is not None:
self.roi.decimate(by_slice=by_slice)
if self.roi_intensity is not None:
self.roi_intensity.decimate(by_slice=by_slice)
if self.roi_morphology is not None:
self.roi_morphology.decimate(by_slice=by_slice)
def interpolate(self, img_obj, settings):
# Skip if image and/or is missing
if img_obj is None or self.roi is None:
return
if settings.img_interpolate.anti_aliasing:
from mirp.imageProcess import gaussian_preprocess_filter
self.roi.set_voxel_grid(voxel_grid=gaussian_preprocess_filter(orig_vox=self.roi.get_voxel_grid(), orig_spacing=self.roi.spacing,
sample_spacing=img_obj.spacing,
param_beta=settings.img_interpolate.smoothing_beta, mode="nearest",
by_slice=settings.general.by_slice))
# Register with image
self.register(img_obj=img_obj)
# Binarise
self.binarise_mask()
def register(self, img_obj: ImageClass, apply_to_self=True):
"""Register roi with image
Do not apply threshold until after interpolation"""
if apply_to_self is False:
roi_copy = self.copy()
roi_copy.register(img_obj=img_obj, apply_to_self=True)
return roi_copy
from mirp.imageProcess import interpolate_to_new_grid
# Skip if image and/or is missing
if img_obj is None or self.roi is None:
return
# Check whether registration is required
registration_required = False
# Mismatch in grid dimension
if np.any([np.abs(np.array(self.roi.size) - np.array(img_obj.size)) > 0.0]):
registration_required = True
# Mismatch in origin
if np.any([np.abs(self.roi.origin - img_obj.origin) > 0.0]):
registration_required = True
# Mismatch in spacing
if np.any([np.abs(self.roi.spacing - img_obj.spacing) > 0.0]):
registration_required = True
if not np.allclose(self.roi.orientation, img_obj.orientation):
raise ValueError("Cannot register segmentation and image object due to different alignments. "
"Please use an external programme to transfer segmentation to the image.")
if registration_required:
# Register roi to image; this transforms the roi grid into
self.roi.size, sample_spacing, voxel_grid, grid_origin = \
interpolate_to_new_grid(orig_dim=self.roi.size,
orig_spacing=self.roi.spacing,
orig_vox=self.roi.get_voxel_grid(),
sample_dim=img_obj.size,
sample_spacing=img_obj.spacing,
grid_origin=np.dot(self.roi.m_affine_inv, np.transpose(img_obj.origin - self.roi.origin)),
order=1,
mode="nearest",
align_to_center=False)
# Update origin before spacing, because computing the origin requires the original affine matrix.
self.roi.origin = self.roi.origin + np.dot(self.roi.m_affine, np.transpose(grid_origin))
# Update spacing and affine matrix.
self.roi.set_spacing(sample_spacing)
# Update voxel grid
self.roi.set_voxel_grid(voxel_grid=voxel_grid)
def binarise_mask(self):
if self.roi is None:
return
if not self.roi.dtype_name == "bool":
self.roi.set_voxel_grid(voxel_grid=np.around(self.roi.get_voxel_grid(), 6) >= np.around(self.incl_threshold, 6))
def generate_masks(self):
""""Generate roi intensity and morphology masks"""
if self.roi is None:
self.roi_intensity = None
self.roi_morphology = None
else:
self.roi_intensity = self.roi.copy()
self.roi_morphology = self.roi.copy()
def update_roi(self):
"""Update region of interest based on intensity and morphological masks"""
if self.roi is None or self.roi_intensity is None or self.roi_morphology is None:
return
self.roi.set_voxel_grid(voxel_grid=np.logical_or(self.roi_intensity.get_voxel_grid(), self.roi_morphology.get_voxel_grid()))
def crop(self, ind_ext_z=None, ind_ext_y=None, ind_ext_x=None,
xy_only=False, z_only=False):
""""Resects roi"""
# Resect masks
if self.roi is not None:
self.roi.crop(ind_ext_z=ind_ext_z,
ind_ext_y=ind_ext_y,
ind_ext_x=ind_ext_x,
xy_only=xy_only,
z_only=z_only)
if self.roi_intensity is not None:
self.roi_intensity.crop(ind_ext_z=ind_ext_z,
ind_ext_y=ind_ext_y,
ind_ext_x=ind_ext_x,
xy_only=xy_only,
z_only=z_only)
if self.roi_morphology is not None:
self.roi_morphology.crop(ind_ext_z=ind_ext_z,
ind_ext_y=ind_ext_y,
ind_ext_x=ind_ext_x,
xy_only=xy_only,
z_only=z_only)
def crop_to_size(self, center, crop_size, xy_only=False):
""""Crops roi to a pre-defined size"""
# Crop masks to size
if self.roi is not None:
self.roi.crop_to_size(center=center, crop_size=crop_size, xy_only=xy_only)
if self.roi_intensity is not None:
self.roi_intensity.crop_to_size(center=center, crop_size=crop_size, xy_only=xy_only)
if self.roi_morphology is not None:
self.roi_morphology.crop_to_size(center=center, crop_size=crop_size, xy_only=xy_only)
def resegmentise_mask(self, img_obj, by_slice, method, settings):
# Resegmentation of the roi map based on grey level values
from skimage.measure import label
from skimage.morphology import remove_small_holes
# Skip if required voxel grids are missing
if img_obj.is_missing or self.roi_intensity is None or self.roi_morphology is None:
return
################################################################################################################
# Resegmentation that affects both intensity and morphological maps
################################################################################################################
# Initialise range
updated_range = np.array([np.nan, np.nan])
if bool(set(method).intersection(["threshold", "range"])):
# Filter out voxels with intensity outside prescribed range
# Local constant
g_thresh = settings.roi_resegment.g_thresh # Threshold values
# Upper threshold
if not np.isnan(g_thresh[1]):
updated_range[1] = copy.deepcopy(g_thresh[1])
# Lower threshold
if not np.isnan(g_thresh[0]):
updated_range[0] = copy.deepcopy(g_thresh[0])
# Set the threshold values as g_range
self.g_range = g_thresh
if bool(set(method).intersection(["sigma", "outlier"])):
# Remove voxels with outlier intensities
# Local constant
sigma = settings.roi_resegment.sigma
img_voxel_grid = img_obj.get_voxel_grid()
roi_voxel_grid = self.roi_intensity.get_voxel_grid()
# Check if the voxel grid is not empty
if np.any(roi_voxel_grid):
# Calculate mean and standard deviation of intensities in roi
mean_int = np.mean(img_voxel_grid[roi_voxel_grid])
sd_int = np.std(img_voxel_grid[roi_voxel_grid])
if not np.isnan(updated_range[0]):
updated_range[0] = np.max([updated_range[0], mean_int - sigma * sd_int])
else:
updated_range[0] = mean_int - sigma * sd_int
if not np.isnan(updated_range[1]):
updated_range[1] = np.min([updated_range[1], mean_int + sigma * sd_int])
else:
updated_range[1] = mean_int + sigma * sd_int
if not np.isnan(updated_range[0]) or not np.isnan(updated_range[1]):
# Update intensity mask
roi_voxel_grid = self.roi_intensity.get_voxel_grid()
if not np.isnan(updated_range[0]):
roi_voxel_grid = np.logical_and((img_obj.get_voxel_grid() >= updated_range[0]), roi_voxel_grid)
if not np.isnan(updated_range[1]):
roi_voxel_grid = np.logical_and((img_obj.get_voxel_grid() <= updated_range[1]), roi_voxel_grid)
# Set roi voxel volume
self.roi_intensity.set_voxel_grid(voxel_grid=roi_voxel_grid)
################################################################################################################
# Resegmentation that affects only morphological maps
################################################################################################################
if bool(set(method).intersection("close_volume")):
# Close internal volumes
from scipy.ndimage import generate_binary_structure, binary_erosion
# Read minimal volume required
max_fill_volume = settings.roi_resegment.max_fill_volume
# Get voxel grid of the roi morphological mask
roi_voxel_grid = self.roi_morphology.get_voxel_grid()
# Determine fill volume (in voxels); if max_fill_volume is less than 0.0, fill all holes
if max_fill_volume < 0.0: fill_volume = np.prod(np.array(self.roi_morphology.size)) + 1.0
else: fill_volume = np.floor(max_fill_volume / np.prod(self.roi_morphology.spacing)) + 1.0
# If the maximum fill volume is smaller than the minimal size of a hole
if fill_volume < 1.0: return None
# Label all non-roi voxels and get label corresponding to voxels outside of the roi
non_roi_label = label(np.pad(roi_voxel_grid, 1, mode="constant", constant_values=0),
background=1, connectivity=3)
outside_label = non_roi_label[0, 0, 0]
# Crop non-roi labels and determine non-roi voxels outside of the mask
non_roi_label = non_roi_label[1:-1, 1:-1, 1:-1]
vox_outside = non_roi_label == outside_label
# Determine mask of voxels which are not internal holes
vox_not_internal = np.logical_or(roi_voxel_grid, vox_outside)
# Check if there are any holes, otherwise continue
if not np.any(~vox_not_internal): return None
if by_slice:
# 2D approach to filling holes
for ii in np.arange(0, self.roi_morphology.size[0]):
# Skip operations on slides that do not contain voxels in the mask or no holes in the slice
if not np.any(roi_voxel_grid[ii, :, :]): continue
if not(np.any(~vox_not_internal[ii, :, :])): continue
# Fill holes up to fill_volume in voxel number
vox_filled = remove_small_holes(vox_not_internal[ii, :, :], min_size=np.int(fill_volume), connectivity=2)
# Update mask by removing outside voxels from the mask
roi_voxel_grid[ii, :, :] = np.squeeze(np.logical_and(vox_filled, ~vox_outside[ii, :, :]))
else:
# 3D approach to filling holes
# Fill holes up to fill_volume in voxel number
vox_filled = remove_small_holes(vox_not_internal, min_size=np.int(fill_volume), connectivity=3)
# Update mask by removing outside voxels from the mask
roi_voxel_grid = np.logical_and(vox_filled, ~vox_outside)
# Update voxel grid
self.roi_morphology.set_voxel_grid(voxel_grid=roi_voxel_grid)
if bool(set(method).intersection("remove_disconnected")):
# Remove disconnected voxels
# Discover prior disconnected volumes from the roi voxel grid
vox_disconnected = label(self.roi.get_voxel_grid(), background=0, connectivity=3)
vox_disconnected_labels = np.unique(vox_disconnected)
# Set up an empty morphological masks
upd_vox_mask = np.full(shape=self.roi_morphology.size, fill_value=False, dtype=np.bool)
# Get the minimum volume fraction for inclusion as voxels
min_vol_fract = settings.roi_resegment.min_vol_fract
# Iterate over disconnected labels
for curr_volume_label in vox_disconnected_labels:
# Skip background
if vox_disconnected_labels == 0: continue
# Mask only current volume, skip if empty
curr_mask = np.logical_and(self.roi_morphology.get_voxel_grid(), vox_disconnected == curr_volume_label)
if not np.any(curr_mask): continue
# Find fully disconnected voxels groups and count them
vox_mask = label(curr_mask, background=0, connectivity=3)
vox_mask_labels, vox_label_count = np.unique(vox_mask, return_counts=True)
# Filter out the background counts
valid_label_id = np.nonzero(vox_mask_labels)
vox_mask_labels = vox_mask_labels[valid_label_id]
vox_label_count = vox_label_count[valid_label_id]
# Normalise to maximum
vox_label_count = vox_label_count / np.max(vox_label_count)
# Select labels fulfilling the minimal size
vox_mask_labels = vox_mask_labels[vox_label_count >= min_vol_fract]
for vox_mask_label_id in vox_mask_labels:
upd_vox_mask += vox_mask == vox_mask_label_id
# Update morphological voxel grid
self.roi_morphology.set_voxel_grid(voxel_grid=upd_vox_mask > 0)
def is_empty(self):
"""Checks whether the roi or one of its masks is empty"""
# Original roi object
if self.roi is not None:
n_roi_voxels = np.int(np.sum(self.roi.get_voxel_grid()))
if n_roi_voxels == 0:
return True
# Roi intensity mask
if self.roi_intensity is not None:
n_roi_int_voxels = np.int(np.sum(self.roi_intensity.get_voxel_grid()))
if n_roi_int_voxels == 0:
return True
# Roi morphological mask
if self.roi_morphology is not None:
n_roi_morph_voxels = np.int(np.sum(self.roi_morphology.get_voxel_grid()))
if n_roi_morph_voxels == 0:
return True
# If none of the above rois was empty, return false
return False
def rotate(self, angle, img_obj):
""" Rotates roi in the y-x plane """
# Register with image prior to rotation
self.register(img_obj=img_obj)
# Rotate roi
self.roi.rotate(angle)
def dilate(self, by_slice, dist=None, vox_dist=None):
from mirp.featureSets.utilities import rep
import scipy.ndimage as ndi
# Skip if the roi does not exist
if self.roi is None:
return
# Check dtype of the roi voxel grid and binarise if necessary
if not self.roi.dtype_name == "bool":
logging.info("Converting roi to boolean before dilation.")
self.binarise_mask()
# Check if any distance is provided for dilation
if vox_dist is None and dist is None:
logging.error("No dilation distance provided.")
# Check whether voxel are isometric
if by_slice: spacing = self.roi.spacing[[1, 2]]
else: spacing = self.roi.spacing
if np.any(spacing - np.max(spacing) != 0.0):
logging.warning("Non-uniform voxel spacing was detected. Roi dilation requires uniform voxel spacing.")
# Derive filter extension and distance
if dist is not None:
base_ext: int = np.max([np.floor(dist / np.max(spacing)).astype(np.int), 0])
else:
base_ext: int = np.int(vox_dist)
dist = vox_dist * np.max(spacing)
# Check if an actual extension is required.
if base_ext > 0:
# Create displacement map
df_base = pd.DataFrame({"x": rep(x=np.arange(-base_ext, base_ext + 1),
each=(2 * base_ext + 1) * (2 * base_ext + 1),
times=1),
"y": rep(x=np.arange(-base_ext, base_ext + 1),
each=2 * base_ext + 1,
times=2 * base_ext + 1),
"z": rep(x=np.arange(-base_ext, base_ext + 1),
each=1,
times=(2 * base_ext + 1) * (2 * base_ext + 1))})
# Calculate distances for displacement map
df_base["dist"] = np.sqrt(np.sum(np.multiply(df_base.loc[:, ("z", "y", "x")].values, self.roi.spacing) ** 2.0, axis=1))
# Identify elements in range
if by_slice: df_base["in_range"] = np.logical_and(df_base.dist <= dist, df_base.z == 0)
else: df_base["in_range"] = df_base.dist <= dist
# Update voxel coordinates to start at [0,0,0]
df_base.loc[:, ["x", "y", "z"]] -= df_base.loc[0, ["x", "y", "z"]]
# Generate geometric filter structure
geom_struct = np.zeros(shape=(np.max(df_base.z) + 1, np.max(df_base.y) + 1,
np.max(df_base.x) + 1), dtype=np.bool)
geom_struct[df_base.z.astype(np.int), df_base.y.astype(np.int), df_base.x.astype(np.int)] = df_base.in_range
# Dilate roi mask amd store voxel grid
self.roi.set_voxel_grid(voxel_grid=ndi.binary_dilation(self.roi.get_voxel_grid(), structure=geom_struct, iterations=1))
else:
logging.info("No dilation: distance %s is too small compared to voxel spacing %s.", str(dist),
str(np.max(spacing)))
def adapt_volume(self, by_slice, vol_grow_fract=None):
import scipy.ndimage
# Skip if the roi does not exist
if self.roi is None:
return
# Check dtype of the roi voxel grid and binarise if necessary
if not self.roi.dtype_name == "bool":
logging.info("Converting roi to boolean before dilation.")
self.binarise_mask()
# Check if any distance is provided for dilation
if vol_grow_fract is None:
logging.error("No dilation volume fraction was provided.")
# Check whether voxels are isometric
if by_slice:
spacing = self.roi.spacing[[1, 2]]
else:
spacing = self.roi.spacing
if np.any(spacing - np.max(spacing) != 0.0):
logging.warning("Non-uniform voxel spacing was detected. Roi volume adaptation requires uniform voxel spacing.")
# Set geometrical structure
geom_struct = scipy.ndimage.generate_binary_structure(3, 1)
if by_slice: geom_struct[(0, 2), :, :] = False # Set structures in different slices to 0
if not vol_grow_fract == 0.0 and not self.is_empty():
# Determine original volume
previous_roi = self.roi.get_voxel_grid()
orig_volume = np.sum(previous_roi)
# Iteratively grow or shrink the volume. The loop terminates through break statements
while True:
if vol_grow_fract > 0.0:
updated_roi = scipy.ndimage.binary_dilation(previous_roi, structure=geom_struct, iterations=1)
else:
updated_roi = scipy.ndimage.binary_erosion(previous_roi, structure=geom_struct, iterations=1)
new_volume = np.sum(updated_roi)
if new_volume == 0:
break
if vol_grow_fract > 0.0 and new_volume/orig_volume - 1.0 >= vol_grow_fract:
break
if vol_grow_fract < 0.0 and new_volume/orig_volume - 1.0 <= vol_grow_fract:
break
# Replace previous roi by the updated roi
previous_roi = updated_roi
# Randomly add/remove border voxels until desired growth/shrinkage is achieved
if not new_volume/orig_volume - 1.0 == vol_grow_fract:
additional_vox = np.abs(int(np.floor(orig_volume * (1.0 + vol_grow_fract) - np.sum(previous_roi))))
if additional_vox > 0:
border_voxel_ind = np.array(np.where(np.logical_xor(previous_roi, updated_roi)))
select_ind = np.random.choice(a=border_voxel_ind.shape[1], size=additional_vox, replace=False)
border_voxel_ind = border_voxel_ind[:, select_ind]
if vol_grow_fract > 0.0:
previous_roi[border_voxel_ind[0, :], border_voxel_ind[1, :], border_voxel_ind[2, :]] = True
else:
previous_roi[border_voxel_ind[0, :], border_voxel_ind[1, :], border_voxel_ind[2, :]] = False
# Set the new roi
self.roi.set_voxel_grid(voxel_grid=previous_roi)
def erode(self, by_slice, eroded_vol_fract=0.8, dist=None, vox_dist=None):
"""" Erosion of the roi segment """
import scipy.ndimage as ndi
# Skip if the roi does not exist
if self.roi is None:
return
# Check whether voxels are booleans
if not self.roi.dtype_name == "bool":
logging.info("Converting roi to boolean before erosion.")
self.binarise_mask()
# Check if any distance is provided for dilation
if vox_dist is None and dist is None:
logging.error("No erosion distance provided.")
# Check whether voxel are isometric
if by_slice: spacing = self.roi.spacing[[1, 2]]
else: spacing = self.roi.spacing
if np.any(spacing - np.max(spacing) != 0.0):
logging.warning("Non-uniform voxel spacing was detected. Roi erosion requires uniform voxel spacing.")
# Set geometrical structure
geom_struct = ndi.generate_binary_structure(3, 1)
if by_slice: geom_struct[(0, 2), :, :] = False # Set structures in different slices to 0
# Set number of erosion steps
if vox_dist is None:
erode_steps = np.max([np.round(np.abs(dist) / np.max(spacing)).astype(np.int), 0])
else:
erode_steps = np.abs(vox_dist.astype(np.int))
dist = vox_dist * np.max(spacing)
if erode_steps > 0:
# Determine initial volume
voxels_prev = self.roi.get_voxel_grid()
vol_init = np.sum(voxels_prev)
# Iterate over erosion steps
for step in np.arange(0, erode_steps):
# Perform erosion
voxels_upd = ndi.binary_erosion(voxels_prev, structure=geom_struct, iterations=1)
# Calculate volume of the eroded volume
vol_curr = np.sum(voxels_upd)
# Stop erosion if the volume shrinks below 80 percent of the original volume due to erosion and return
# return voxels from the previous erosion step.
if vol_curr * 1.0 / vol_init < eroded_vol_fract:
voxels_upd = voxels_prev
break
else:
voxels_prev = voxels_upd
# Set updated voxels
self.roi.set_voxel_grid(voxel_grid=voxels_upd)
else:
logging.info("No erosion: distance %s is too small compared to voxel spacing %s.", str(dist), str(np.max(spacing)))
def decode_voxel_grid(self):
"""Converts run length encoded grids to conventional volumes"""
# Decode main ROI object
if self.roi is not None:
self.roi.decode_voxel_grid()
# Decode intensity and morphological masks
if self.roi_intensity is not None:
self.roi_intensity.decode_voxel_grid()
if self.roi_morphology is not None:
self.roi_morphology.decode_voxel_grid()
def as_pandas_dataframe(self, img_obj, intensity_mask=False, morphology_mask=False, distance_map=False, by_slice=False):
"""Converts the image and roi voxel grids to a pandas dataframe for further processing"""
# Return None if the image and/or ROI are missing
if img_obj.is_missing or self.roi is None:
return None
# Check if the masks exist and assign if not
if intensity_mask and self.roi_intensity is None:
self.roi_intensity = self.roi.copy()
if (morphology_mask or distance_map) and self.roi_morphology is None:
self.roi_morphology = self.roi.copy()
# Create table from test object
img_dims = img_obj.size
index_id = np.arange(start=0, stop=np.prod(img_dims))
coords = np.unravel_index(indices=index_id, dims=img_dims)
df_img = pd.DataFrame({"index_id": index_id,
"g": np.ravel(img_obj.get_voxel_grid()),
"x": coords[2],
"y": coords[1],
"z": coords[0]})
if intensity_mask:
df_img["roi_int_mask"] = np.ravel(self.roi_intensity.get_voxel_grid()).astype(np.bool)
if morphology_mask:
df_img["roi_morph_mask"] = np.ravel(self.roi_morphology.get_voxel_grid()).astype(np.bool)
if distance_map:
# Calculate distance by sequential border erosion
from scipy.ndimage import generate_binary_structure, binary_erosion
# Set up distance map and morphological voxel grid
dist_map = np.zeros(img_dims)
morph_voxel_grid = self.roi_morphology.get_voxel_grid()
if by_slice:
# Distances are determined in 2D
binary_struct = generate_binary_structure(rank=2, connectivity=1)
# Iterate over slices
for ii in np.arange(0, img_dims[0]):
# Calculate distance by sequential border erosion
roi_eroded = np.squeeze(morph_voxel_grid[ii, :, :])
# Iterate distance from border
while np.sum(roi_eroded) > 0:
roi_eroded = binary_erosion(roi_eroded, structure=binary_struct)
dist_map[ii, :, :] += roi_eroded * 1
else:
# Distances are determined in 3D
binary_struct = generate_binary_structure(rank=3, connectivity=1)
# Copy of roi morphology mask
roi_eroded = copy.deepcopy(morph_voxel_grid)
# Incrementally erode the morphological mask
while np.sum(roi_eroded) > 0:
roi_eroded = binary_erosion(roi_eroded, structure=binary_struct)
dist_map += roi_eroded * 1
# Update distance from border, as minimum distance is 1
dist_map[morph_voxel_grid] += 1
# Add distance map to table
df_img["border_distance"] = np.ravel(dist_map).astype(np.int32)
return df_img
def compute_diagnostic_features(self, img_obj, append_str=""):
""" Creates diagnostic features for the ROI """
# Set feature names
feat_names = ["int_map_dim_x", "int_map_dim_y", "int_map_dim_z", "int_bb_dim_x", "int_bb_dim_y", "int_bb_dim_z",
"int_vox_dim_x", "int_vox_dim_y", "int_vox_dim_z", "int_vox_count", "int_mean_int", "int_min_int", "int_max_int",
"mrp_map_dim_x", "mrp_map_dim_y", "mrp_map_dim_z", "mrp_bb_dim_x", "mrp_bb_dim_y", "mrp_bb_dim_z",
"mrp_vox_dim_x", "mrp_vox_dim_y", "mrp_vox_dim_z", "mrp_vox_count", "mrp_mean_int", "mrp_min_int",
"mrp_max_int"]
# Create pandas dataframe with one row and feature columns
df = pd.DataFrame(np.full(shape=(1, len(feat_names)), fill_value=np.nan))
df.columns = feat_names
# Skip further analysis if the image and/or roi are missing
if img_obj.is_missing or self.roi is None:
return df
# Register with image on function call
roi_copy = self.register(img_obj, apply_to_self=False)
# Binarise (if required)
roi_copy.binarise_mask()
# Make copies of intensity and morphological masks (if required)
if roi_copy.roi_intensity is None:
roi_copy.roi_intensity = roi_copy.roi
if roi_copy.roi_morphology is None:
roi_copy.roi_morphology = roi_copy.roi
# Get image and roi voxel grids
img_voxel_grid = img_obj.get_voxel_grid()
int_voxel_grid = roi_copy.roi_intensity.get_voxel_grid()
mrp_voxel_grid = roi_copy.roi_morphology.get_voxel_grid()
# Compute bounding boxes
int_bounding_box_dim = np.squeeze(np.diff(roi_copy.get_bounding_box(roi_voxel_grid=int_voxel_grid), axis=0) + 1)
mrp_bounding_box_dim = np.squeeze(np.diff(roi_copy.get_bounding_box(roi_voxel_grid=mrp_voxel_grid), axis=0) + 1)
# Set intensity mask features
df["int_map_dim_x"] = roi_copy.roi_intensity.size[2]
df["int_map_dim_y"] = roi_copy.roi_intensity.size[1]
df["int_map_dim_z"] = roi_copy.roi_intensity.size[0]
df["int_bb_dim_x"] = int_bounding_box_dim[2]
df["int_bb_dim_y"] = int_bounding_box_dim[1]
df["int_bb_dim_z"] = int_bounding_box_dim[0]
df["int_vox_dim_x"] = roi_copy.roi_intensity.spacing[2]
df["int_vox_dim_y"] = roi_copy.roi_intensity.spacing[1]
df["int_vox_dim_z"] = roi_copy.roi_intensity.spacing[0]
df["int_vox_count"] = np.sum(int_voxel_grid)
df["int_mean_int"] = np.mean(img_voxel_grid[int_voxel_grid])
df["int_min_int"] = np.min(img_voxel_grid[int_voxel_grid])
df["int_max_int"] = np.max(img_voxel_grid[int_voxel_grid])
# Set morphological mask features
df["mrp_map_dim_x"] = roi_copy.roi_morphology.size[2]
df["mrp_map_dim_y"] = roi_copy.roi_morphology.size[1]
df["mrp_map_dim_z"] = roi_copy.roi_morphology.size[0]
df["mrp_bb_dim_x"] = mrp_bounding_box_dim[2]
df["mrp_bb_dim_y"] = mrp_bounding_box_dim[1]
df["mrp_bb_dim_z"] = mrp_bounding_box_dim[0]
df["mrp_vox_dim_x"] = roi_copy.roi_morphology.spacing[2]
df["mrp_vox_dim_y"] = roi_copy.roi_morphology.spacing[1]
df["mrp_vox_dim_z"] = roi_copy.roi_morphology.spacing[0]
df["mrp_vox_count"] = np.sum(mrp_voxel_grid)
df["mrp_mean_int"] = np.mean(img_voxel_grid[mrp_voxel_grid])
df["mrp_min_int"] = np.min(img_voxel_grid[mrp_voxel_grid])
df["mrp_max_int"] = np.max(img_voxel_grid[mrp_voxel_grid])
# Update column names
df.columns = ["_".join(["diag", feature, append_str]).strip("_") for feature in df.columns]
del roi_copy
self.diagnostic_list += [df]
def get_bounding_box(self, roi_voxel_grid):
# Calculates coordinates of ROI bounding box
z_ind, y_ind, x_ind = np.where(roi_voxel_grid)
max_ind = np.array((np.max(z_ind), np.max(y_ind), np.max(x_ind)))
min_ind = np.array((np.min(z_ind), np.min(y_ind), np.min(x_ind)))
del z_ind, y_ind, x_ind
return min_ind, max_ind
def get_center_slice(self):
""" Identify location of the central slice in the roi """
# Return a NaN if no roi is present
if self.roi is None:
return np.nan
# Determine indices of voxels included in the roi
z_ind, y_ind, x_ind = np.where(self.roi.get_voxel_grid())
z_center = (np.max(z_ind) + np.min(z_ind)) // 2
return z_center
def get_all_slices(self):
""" Identify location of all slices in the roi """
# Return NaN in case the roi is missing
if self.roi is None:
return np.array([np.nan])
z_ind, y_ind, x_ind = np.where(self.roi.get_voxel_grid())
return np.unique(z_ind)
def export(self, img_obj, file_path):
"""
Export roi to file
:param img_obj:
:param file_path:
:return:
"""
roi_str_components = [img_obj.get_export_descriptor()]
roi_str_components += [self.get_export_descriptor()]
# Write morphological and intensity roi
if self.roi_morphology is not None and self.roi_intensity is not None:
self.roi_morphology.write(file_path=file_path, file_name="_".join(roi_str_components + ["morph.nii.gz"]))
self.roi_intensity.write(file_path=file_path, file_name="_".join(roi_str_components + ["int.nii.gz"]))
elif self.roi is not None:
self.roi.write(file_path=file_path, file_name="_".join(roi_str_components + ["nii.gz"]))
else:
return
def get_export_descriptor(self):
"""
Generates an export string for identifying a file
:return: export string
"""
descr_list = []
if self.adapt_size != 0.0:
# Volume adaptation
descr_list += ["vol",
str(self.adapt_size)]
if self.svx_randomisation_id != -1:
# Contour randomisation
descr_list += ["svx",
str(self.svx_randomisation_id)]
descr_list += [self.name]
return "_".join(descr_list)
def get_slices(self, slice_number=None):
# Extract roi objects for each slice
roi_obj_list = []
# Create a copy of the current object
base_roi_obj = self.copy(drop_image=True)
# Remove attributes that need to be set
base_roi_obj.roi = None
base_roi_obj.roi_intensity = None
base_roi_obj.roi_morphology = None
if slice_number is None:
# Extract mask for each slice. Copy the base roi object.
if self.roi is not None:
roi_slices = self.roi.get_slices()
if self.roi_intensity is not None:
roi_int_slices = self.roi_intensity.get_slices()
else:
roi_int_slices = None
if self.roi_morphology is not None:
roi_morph_slices = self.roi_morphology.get_slices()
else:
roi_morph_slices = None
# Add masks to a roi object for each slice
for ii in np.arange(self.roi.size[0]):
slice_roi_obj = copy.deepcopy(base_roi_obj)
if self.roi is not None:
slice_roi_obj.roi = roi_slices[ii]
if self.roi_intensity is not None:
slice_roi_obj.roi_intensity = roi_int_slices[ii]
if self.roi_morphology is not None:
slice_roi_obj.roi_morphology = roi_morph_slices[ii]
# Add to list
roi_obj_list += [slice_roi_obj]
else:
# Extract a single slice. Copy the base roi object.
slice_roi_obj = copy.deepcopy(base_roi_obj)
# Add the mask for the requested slice
if self.roi is not None:
slice_roi_obj.roi = self.roi.get_slices(slice_number=slice_number)[0]
if self.roi_intensity is not None:
slice_roi_obj.roi_intensity = self.roi_intensity.get_slices(slice_number=slice_number)[0]
if self.roi_morphology is not None:
slice_roi_obj.roi_morphology = self.roi_morphology.get_slices(slice_number=slice_number)[0]
# Add to list
roi_obj_list += [slice_roi_obj]
return roi_obj_list
def drop_image(self):
"""Drops image, e.g. to free up memory."""
if self.roi is not None:
self.roi.drop_image()
if self.roi_intensity is not None:
self.roi_intensity.drop_image()
if self.roi_morphology is not None:
self.roi_morphology.drop_image()
def drop_metadata(self):
self.metadata = None
if self.roi is not None:
self.roi.drop_metadata()
if self.roi_intensity is not None:
self.roi_intensity.drop_metadata()
if self.roi_morphology is not None:
self.roi_morphology.drop_metadata()
def write_dicom(self, file_path, file_name="RS.dcm"):
import os
if self.metadata is None:
return None
# Check if the write folder exists
if not os.path.isdir(file_path):
if os.path.isfile(file_path):
# Check if the write folder is a file.
raise IOError(f"{file_path} is an existing file, not a directory. No DICOM images were exported.")
else:
os.makedirs(file_path, exist_ok=True)
self.metadata.save_as(filename=os.path.join(file_path, file_name), write_like_original=False)
def get_metadata(self, tag, tag_type, default=None):
# Do not attempt to read the metadata if no metadata is present.
if self.metadata is None:
return
return get_pydicom_meta_tag(dcm_seq=self.metadata, tag=tag, tag_type=tag_type, default=default)
def set_metadata(self, tag, value, force_vr=None):
# Do not update the metadata if no metadata is present.
if self.metadata is None:
return None
set_pydicom_meta_tag(dcm_seq=self.metadata, tag=tag, value=value, force_vr=force_vr)
def has_metadata(self, tag):
if self.metadata is None:
return None
else:
return get_pydicom_meta_tag(dcm_seq=self.metadata, tag=tag, test_tag=True)
def rename(self, new):
if self.metadata is not None:
# Obtain the old name
old = self.name
if not self.has_metadata(tag=(0x3006, 0x0020)):
raise ValueError(f"The DICOM metaheader does not contain a Structure Set ROI sequence.")
# Iterate over roi elements in the roi sequence
for ii, roi_element in enumerate(self.metadata[0x3006, 0x0020]):
# Find ROI name that matches the old name
if get_pydicom_meta_tag(dcm_seq=roi_element, tag=(0x3006, 0x0026), tag_type="str") == old:
set_pydicom_meta_tag(dcm_seq=roi_element, tag=(0x3006, 0x0026), value=new)
# Assign a new name
self.name = new
else:
# Assign a new name
self.name = new
def read_dicom_image_series(image_folder, modality=None, series_uid=None):
# Obtain a list with image files
file_list = _find_dicom_image_series(image_folder=image_folder,
allowed_modalities=["CT", "PT", "MR"],
modality=modality,
series_uid=series_uid)
# Obtain slice positions for each file
file_table = pd.DataFrame({
"file_name": file_list,
"position_z": 0.0,
"position_y": 0.0,
"position_x": 0.0,
"sop_instance_uid": ""
})
image_position_x = []
image_position_y = []
image_position_z = []
sop_instance_uid = []
for file_name in file_list:
# Load the dicom header
dcm = pydicom.dcmread(os.path.join(image_folder, file_name),
stop_before_pixels=True,
force=True)
# Find the origin of each slice.
slice_origin = get_pydicom_meta_tag(dcm_seq=dcm,
tag=(0x0020, 0x0032),
tag_type="mult_float",
default=np.array([0.0, 0.0,
0.0]))[::-1]
# Update with slice positions
image_position_x += [slice_origin[2]]
image_position_y += [slice_origin[1]]
image_position_z += [slice_origin[0]]
# Find the sop instance UID of each slice.
slice_sop_instance_uid = get_pydicom_meta_tag(dcm_seq=dcm,
tag=(0x0008, 0x0018),
tag_type="str")
# Update with the slice SOP instance UID.
sop_instance_uid += [slice_sop_instance_uid]
# Order ascending position (DICOM: z increases from feet to head)
file_table = pd.DataFrame({
"file_name": file_list,
"position_z": image_position_z,
"position_y": image_position_y,
"position_x": image_position_x,
"sop_instance_uid": sop_instance_uid
}).sort_values(by=["position_z", "position_y", "position_x"])
# Obtain DICOM metadata from the bottom slice. This will be used to fill most of the different details.
dcm = pydicom.dcmread(os.path.join(image_folder,
file_table.file_name.values[0]),
stop_before_pixels=True,
force=True)
# Find the number of rows (y) and columns (x) in the data set.
n_x = get_pydicom_meta_tag(dcm_seq=dcm,
tag=(0x0028, 0x011),
tag_type="int")
n_y = get_pydicom_meta_tag(dcm_seq=dcm,
tag=(0x0028, 0x010),
tag_type="int")
# Create an empty voxel grid. Use z, y, x ordering for consistency within MIRP.
voxel_grid = np.zeros((len(file_table), n_y, n_x), dtype=np.float32)
# Read all dicom slices in order.
slice_dcm_list = [
pydicom.dcmread(os.path.join(image_folder, file_name),
stop_before_pixels=False,
force=True)
for file_name in file_table.file_name.values
]
# Iterate over the different slices to fill out the voxel_grid.
for ii, file_name in enumerate(file_table.file_name.values):
# Read the dicom file and extract the slice grid
slice_dcm = slice_dcm_list[ii]
slice_grid = slice_dcm.pixel_array.astype(np.float32)
# Update with scale and intercept. These may change per slice.
rescale_intercept = get_pydicom_meta_tag(dcm_seq=slice_dcm,
tag=(0x0028, 0x1052),
tag_type="float",
default=0.0)
rescale_slope = get_pydicom_meta_tag(dcm_seq=slice_dcm,
tag=(0x0028, 0x1053),
tag_type="float",
default=1.0)
slice_grid = slice_grid * rescale_slope + rescale_intercept
# Convert all images to SUV at admin
if get_pydicom_meta_tag(dcm_seq=dcm,
tag=(0x0008, 0x0060),
tag_type="str") == "PT":
suv_conversion_object = SUVscalingObj(dcm=slice_dcm)
scale_factor = suv_conversion_object.get_scale_factor(
suv_normalisation="bw")
# Convert to SUV
slice_grid *= scale_factor
# Update the DICOM header
slice_dcm = suv_conversion_object.update_dicom_header(
dcm=slice_dcm)
# Store in voxel grid
voxel_grid[ii, :, :] = slice_grid
# Obtain the image origin from the dicom header (note: z, y, x order)
image_origin = get_pydicom_meta_tag(dcm_seq=dcm,
tag=(0x0020, 0x0032),
tag_type="mult_float",
default=np.array([0.0, 0.0,
0.0]))[::-1]
# Obtain the image spacing from the dicom header and slice positions.
image_pixel_spacing = get_pydicom_meta_tag(dcm_seq=dcm,
tag=(0x0028, 0x0030),
tag_type="mult_float")
image_slice_thickness = get_pydicom_meta_tag(dcm_seq=dcm,
tag=(0x0018, 0x0050),
tag_type="float",
default=None)
if len(file_table) > 1:
# Compute the distance between the origins of the slices. This is the slice spacing.
image_slice_spacing = np.median(
np.sqrt(
np.sum(np.array([
np.power(np.diff(file_table.position_z.values), 2.0),
np.power(np.diff(file_table.position_y.values), 2.0),
np.power(np.diff(file_table.position_x.values), 2.0)
]),
axis=0)))
if image_slice_thickness is None:
# TODO: Update slice thickness tag in dcm
pass
else:
# Warn the user if there is a mismatch between slice thickness and the actual slice spacing.
if not np.around(image_slice_thickness - image_slice_spacing,
decimals=3) == 0.0:
warnings.warn(
f"Mismatch between slice thickness ({image_slice_thickness}) and actual slice spacing ({image_slice_spacing}). The actual slice spacing will be "
f"used.", UserWarning)
elif image_slice_thickness is not None:
# There is only one slice, and we use the slice thickness as parameter.
image_slice_spacing = image_slice_thickness
else:
# There is only one slice and the slice thickness is unknown. In this situation, we use the pixel spacing
image_slice_spacing = np.max(image_pixel_spacing)
# Combine pixel spacing and slice spacing into the voxel spacing, using z, y, x order.
image_spacing = np.array(
[image_slice_spacing, image_pixel_spacing[1], image_pixel_spacing[0]])
# Obtain image orientation (Xx, Xy, Xz, Yx, Yy, Yz; see DICOM C.7.6.2 Image Plane Module)
image_orientation = get_pydicom_meta_tag(dcm_seq=dcm,
tag=(0x0020, 0x0037),
tag_type="mult_float")
# Add (Zx, Zy, Zz)
if len(file_table) > 1:
# Compute distance between subsequent origins.
slice_origin_distance = np.sqrt(
np.power(np.diff(file_table.position_x.values), 2.0) +
np.power(np.diff(file_table.position_y.values), 2.0) +
np.power(np.diff(file_table.position_z.values), 2.0))
# Find unique distance values.
slice_origin_distance = np.unique(np.around(slice_origin_distance, 3))
# If there is more than one value, this means that there is an unexpected shift in origins.
if len(slice_origin_distance) > 1:
raise ValueError(
f"Inconsistent distance between slice origins of subsequent slices: "
f"{slice_origin_distance}. Slices cannot be aligned correctly. This is likely due to "
f"missing slices.")
z_orientation = np.array([
np.median(np.diff(file_table.position_x.values)),
np.median(np.diff(file_table.position_y.values)),
np.median(np.diff(file_table.position_z.values))
]) / image_slice_spacing
# Append orientation.
image_orientation += z_orientation.tolist()
else:
image_orientation += [0.0, 0.0, 1.0]
# Revert to z, y, x order
image_orientation = image_orientation[::-1]
# Create an ImageClass object and store dicom meta-data
img_obj = ImageClass(voxel_grid=voxel_grid,
origin=image_origin,
spacing=image_spacing,
orientation=image_orientation,
modality=get_pydicom_meta_tag(dcm_seq=dcm,
tag=(0x0008, 0x0060),
tag_type="str"),
spat_transform="base",
no_image=False,
metadata=slice_dcm_list[0],
slice_table=file_table)
return img_obj
def read_dicom_image_series(image_folder, modality=None, series_uid=None):
# Obtain a list with image files
file_list = _find_dicom_image_series(image_folder=image_folder,
allowed_modalities=["CT", "PT", "MR"],
modality=modality,
series_uid=series_uid)
# Obtain slice positions for each file
image_position_z = []
for file_name in file_list:
# Read DICOM header
dcm = pydicom.dcmread(os.path.join(image_folder, file_name),
stop_before_pixels=True,
force=True,
specific_tags=[Tag(0x0020, 0x0032)])
# Obtain the z position
image_position_z += [
get_pydicom_meta_tag(dcm_seq=dcm,
tag=(0x0020, 0x0032),
tag_type="mult_float")[2]
]
# Order ascending position (DICOM: z increases from feet to head)
file_table = pd.DataFrame({
"file_name": file_list,
"position_z": image_position_z
}).sort_values(by="position_z")
# Obtain DICOM metadata from the bottom slice. This will be used to fill out all the different details.
dcm = pydicom.dcmread(os.path.join(image_folder,
file_table.file_name.values[0]),
stop_before_pixels=True,
force=True)
# Find the number of rows (y) and columns (x) in the data set.
n_x = get_pydicom_meta_tag(dcm_seq=dcm,
tag=(0x0028, 0x011),
tag_type="int")
n_y = get_pydicom_meta_tag(dcm_seq=dcm,
tag=(0x0028, 0x010),
tag_type="int")
# Create an empty voxel grid. Use z, y, x ordering for consistency within MIRP.
voxel_grid = np.zeros((len(file_table), n_y, n_x), dtype=np.float32)
# Read all dicom slices in order.
slice_dcm_list = [
pydicom.dcmread(os.path.join(image_folder, file_name),
stop_before_pixels=False,
force=True)
for file_name in file_table.file_name.values
]
# Iterate over the different slices to fill out the voxel_grid.
for ii, file_name in enumerate(file_table.file_name.values):
# Read the dicom file and extract the slice grid
slice_dcm = slice_dcm_list[ii]
slice_grid = slice_dcm.pixel_array.astype(np.float32)
# Update with scale and intercept. These may change per slice.
rescale_intercept = get_pydicom_meta_tag(dcm_seq=slice_dcm,
tag=(0x0028, 0x1052),
tag_type="float",
default=0.0)
rescale_slope = get_pydicom_meta_tag(dcm_seq=slice_dcm,
tag=(0x0028, 0x1053),
tag_type="float",
default=1.0)
slice_grid = slice_grid * rescale_slope + rescale_intercept
# Convert all images to SUV at admin
if get_pydicom_meta_tag(dcm_seq=dcm,
tag=(0x0008, 0x0060),
tag_type="str") == "PT":
suv_conversion_object = SUVscalingObj(dcm=slice_dcm)
scale_factor = suv_conversion_object.get_scale_factor(
suv_normalisation="bw")
# Convert to SUV
slice_grid *= scale_factor
# Update the DICOM header
slice_dcm = suv_conversion_object.update_dicom_header(
dcm=slice_dcm)
# Store in voxel grid
voxel_grid[ii, :, :] = slice_grid
# Obtain the image origin from the dicom header (note: z, y, x order)
image_origin = get_pydicom_meta_tag(dcm_seq=dcm,
tag=(0x0020, 0x0032),
tag_type="mult_float",
default=np.array([0.0, 0.0,
0.0]))[::-1]
# Obtain the image spacing from the dicom header and slice positions.
image_pixel_spacing = get_pydicom_meta_tag(dcm_seq=dcm,
tag=(0x0028, 0x0030),
tag_type="mult_float")
image_slice_thickness = get_pydicom_meta_tag(dcm_seq=dcm,
tag=(0x0018, 0x0050),
tag_type="float",
default=None)
if len(file_table) > 1:
# Slice spacing can be determined from the slice positions
image_slice_spacing = np.median(
np.abs(np.diff(file_table.position_z.values)))
if image_slice_thickness is None:
# TODO: Update slice thickness tag in dcm
pass
else:
# Warn the user if there is a mismatch between slice thickness and the actual slice spacing.
if not np.around(image_slice_thickness - image_slice_spacing,
decimals=5) == 0.0:
warnings.warn(
f"Mismatch between slice thickness ({image_slice_thickness}) and actual slice spacing ({image_slice_spacing}). The actual slice spacing will be "
f"used.", UserWarning)
elif image_slice_thickness is not None:
# There is only one slice, and we use the slice thickness as parameter.
image_slice_spacing = image_slice_thickness
else:
# There is only one slice and the slice thickness is unknown. In this situation, we use the pixel spacing
image_slice_spacing = np.max(image_pixel_spacing)
# Combine pixel spacing and slice spacing into the voxel spacing, using z, y, x order.
image_spacing = np.array(
[image_slice_spacing, image_pixel_spacing[1], image_pixel_spacing[0]])
# Obtain image orientation and add the 3rd dimension
image_orientation = get_pydicom_meta_tag(dcm_seq=dcm,
tag=(0x0020, 0x0037),
tag_type="mult_float")
image_orientation += [0.0, 0.0, 1.0]
# Revert to z, y, x order
image_orientation = image_orientation[::-1]
# Create an ImageClass object and store dicom meta-data
img_obj = ImageClass(voxel_grid=voxel_grid,
origin=image_origin,
spacing=image_spacing,
slice_z_pos=file_table.position_z.values,
orientation=image_orientation,
modality=get_pydicom_meta_tag(dcm_seq=dcm,
tag=(0x0008, 0x0060),
tag_type="str"),
spat_transform="base",
no_image=False,
metadata=slice_dcm_list[0],
metadata_sop_instances=[
get_pydicom_meta_tag(dcm_seq=slice_dcm,
tag=(0x0008, 0x0018),
tag_type="str")
for slice_dcm in slice_dcm_list
])
return img_obj
