def build_distance_layers(cyt_coord, nuc_coord, rna_coord, normalized=True):
"""Compute distance layers as input for the model.
Parameters
----------
cyt_coord : np.ndarray, np.int64
Array of cytoplasm boundaries coordinates with shape (nb_points, 2).
nuc_coord : np.ndarray, np.int64
Array of nucleus boundaries coordinates with shape (nb_points, 2).
rna_coord : np.ndarray, np.int64
Array of mRNAs coordinates with shape (nb_points, 2) or
(nb_points, 3).
normalized : bool
Normalized the layers between 0 and 1.
Returns
-------
distance_cyt : np.ndarray, np.float32
A 2-d tensor with shape (y, x) showing distance to the cytoplasm
border. Normalize between 0 and 1 if 'normalized' True.
distance_nuc : np.ndarray, np.float32
A 2-d tensor with shape (y, x) showing distance to the nucleus border.
Normalize between 0 and 1 if 'normalized' True.
"""
# check parameters
stack.check_array(cyt_coord,
ndim=2,
dtype=[np.int64])
if nuc_coord is not None:
stack.check_array(nuc_coord,
ndim=2,
dtype=[np.int64])
if rna_coord is not None:
stack.check_array(rna_coord,
ndim=2,
dtype=[np.int64])
stack.check_parameter(normalized=bool)
# build surface binary matrices from coordinates
cyt_surface, nuc_surface, rna_layer, _ = stack.from_coord_to_surface(
cyt_coord=cyt_coord,
nuc_coord=nuc_coord,
rna_coord=rna_coord)
# compute distances map for cytoplasm and nucleus
cyt_distance = ndi.distance_transform_edt(cyt_surface)
nuc_distance_ = ndi.distance_transform_edt(~nuc_surface)
nuc_distance = cyt_surface * nuc_distance_
if normalized:
# cast to np.float32 and normalize it between 0 and 1
cyt_distance = cyt_distance / cyt_distance.max()
nuc_distance = nuc_distance / nuc_distance.max()
# cast layer in float32
cyt_distance = stack.cast_img_float32(cyt_distance)
nuc_distance = stack.cast_img_float32(nuc_distance)
rna_layer = stack.cast_img_float32(rna_layer)
return cyt_distance, nuc_distance, rna_layer
def build_boundaries_layers(cyt_coord, nuc_coord, rna_coord):
"""
Parameters
----------
cyt_coord : np.ndarray, np.int64
Array of cytoplasm boundaries coordinates with shape (nb_points, 2).
nuc_coord : np.ndarray, np.int64
Array of nucleus boundaries coordinates with shape (nb_points, 2).
rna_coord : np.ndarray, np.int64
Array of mRNAs coordinates with shape (nb_points, 2) or
(nb_points, 3).
Returns
-------
cyt_boundaries : np.ndarray, np.float32
A 2-d binary tensor with shape (y, x) showing cytoplasm boundaries.
border.
nuc_boundaries : np.ndarray, np.float32
A 2-d binary tensor with shape (y, x) showing nucleus boundaries.
rna_layer : np.ndarray, np.float32
Binary image of mRNAs localizations with shape (y, x).
"""
# check parameters
stack.check_array(cyt_coord,
ndim=2,
dtype=[np.int64])
if nuc_coord is not None:
stack.check_array(nuc_coord,
ndim=2,
dtype=[np.int64])
if rna_coord is not None:
stack.check_array(rna_coord,
ndim=2,
dtype=[np.int64])
# build surface binary matrices from coordinates
cyt_surface, nuc_surface, rna_layer, _ = stack.from_coord_to_surface(
cyt_coord=cyt_coord,
nuc_coord=nuc_coord,
rna_coord=rna_coord)
# from surface binary matrices to boundaries binary matrices
cyt_boundaries = stack.from_surface_to_boundaries(cyt_surface)
nuc_boundaries = stack.from_surface_to_boundaries(nuc_surface)
# cast layer in float32
cyt_boundaries = stack.cast_img_float32(cyt_boundaries)
nuc_boundaries = stack.cast_img_float32(nuc_boundaries)
rna_layer = stack.cast_img_float32(rna_layer)
return cyt_boundaries, nuc_boundaries, rna_layer
def prepare_coordinate_data(cyt_coord, nuc_coord, rna_coord):
"""
Parameters
----------
cyt_coord
nuc_coord
rna_coord
Returns
-------
"""
# convert coordinates in binary mask surfaces
mask_cyt, mask_nuc, _, rna_coord = stack.from_coord_to_surface(
cyt_coord=cyt_coord,
nuc_coord=nuc_coord,
rna_coord=rna_coord,
external_coord=True)
# get mask cytoplasm outside nucleus
mask_cyt_out = mask_cyt.copy()
mask_cyt_out[mask_nuc] = False
# compute distance maps for the cytoplasm and the nucleus
distance_cyt = ndi.distance_transform_edt(mask_cyt)
distance_nuc_ = ndi.distance_transform_edt(~mask_nuc)
distance_nuc = mask_cyt * distance_nuc_
# cast distance map in float32
distance_cyt = distance_cyt.astype(np.float32)
distance_nuc = distance_nuc.astype(np.float32)
# normalize distance maps between 0 and 1
distance_cyt_normalized = distance_cyt / distance_cyt.max()
distance_cyt_normalized = stack.cast_img_float32(distance_cyt_normalized)
distance_nuc_normalized = distance_nuc / distance_nuc.max()
distance_nuc_normalized = stack.cast_img_float32(distance_nuc_normalized)
# get rna outside nucleus
mask_rna_in = mask_nuc[rna_coord[:, 1], rna_coord[:, 2]]
rna_coord_out = rna_coord[~mask_rna_in]
# get centroids
centroid_cyt = _get_centroid_surface(mask_cyt)
centroid_nuc = _get_centroid_surface(mask_nuc)
if len(rna_coord) == 0:
centroid_rna = centroid_cyt.copy()
else:
centroid_rna = _get_centroid_rna(rna_coord)
if len(rna_coord_out) == 0:
centroid_rna_out = centroid_cyt.copy()
else:
centroid_rna_out = _get_centroid_rna(rna_coord_out)
# get centroid distance maps
distance_cyt_centroid = _get_centroid_distance_map(centroid_cyt, mask_cyt)
distance_nuc_centroid = _get_centroid_distance_map(centroid_nuc, mask_cyt)
distance_rna_out_centroid = _get_centroid_distance_map(
centroid_rna_out, mask_cyt)
prepared_inputs = (mask_cyt, mask_nuc, mask_cyt_out, distance_cyt,
distance_nuc, distance_cyt_normalized,
distance_nuc_normalized, rna_coord_out, centroid_cyt,
centroid_nuc, centroid_rna, centroid_rna_out,
distance_cyt_centroid, distance_nuc_centroid,
distance_rna_out_centroid)
return prepared_inputs