def compute_cell_volume(self):
"""
computes cell volume in in real pixel size using the cell mask
"""
cell_mask = self.get_cell_mask()
height_map = self.adjust_height_map()
cell_volume = height_map[np.where(cell_mask[:] == 1)].sum()
if (len(self.get_multiple_nucleus_centroid())) > 1:
return cell_volume / len(
self.get_multiple_nucleus_centroid()) * helpers.volume_coeff()
else:
return cell_volume * helpers.volume_coeff()
def compute_density_per_quadrant(self,
mtoc_quad,
quadrant_mask,
quadrants_num=4) -> np.ndarray:
"""
compute volumic density per quadrant;
return an array of values of density paired with the MTOC presence flag (0/1)
"""
IF = self.compute_cytoplasmic_intensities()
height_map = self.adjust_height_map(cytoplasm=True)
density_per_quadrant = np.zeros((quadrants_num, 2))
# mark the MTOC quadrant
density_per_quadrant[mtoc_quad - 1, 1] = 1
for quad_num in range(quadrants_num):
height = np.sum(height_map[quadrant_mask == quad_num + 1])
quadrant_volume = height * helpers.volume_coeff()
density_per_quadrant[quad_num, 0] = np.sum(
IF[quadrant_mask == (quad_num + 1)]) / quadrant_volume
if density_per_quadrant[:, 1].sum() != 1.0:
raise (RuntimeError,
"error in the MTOC quadrant detection for image %s" %
self._path)
return density_per_quadrant
def compute_density_per_quadrant(self,
mtoc_quad,
quadrant_mask,
quadrants_num=4) -> np.ndarray:
"""
compute volumic density per quadrant;
return an array of values of density paired with the MTOC presence flag (0/1)
"""
volume_coeff = helpers.volume_coeff()
height_map = self.adjust_height_map(cytoplasm=True)
spots = self.get_cytoplasmic_spots()
density_per_quadrant = np.zeros((quadrants_num, 2))
for spot in spots:
spot_quad = quadrant_mask[spot[1], spot[0]]
if spot_quad == 0: continue
density_per_quadrant[spot_quad - 1, 0] += 1
# mark the mtoc quadrant
density_per_quadrant[mtoc_quad - 1, 1] = 1
for quad_num in range(quadrants_num):
quadrant_volume = np.sum(
height_map[quadrant_mask == quad_num + 1]) * volume_coeff
density_per_quadrant[quad_num,
0] = density_per_quadrant[quad_num,
0] / quadrant_volume
if density_per_quadrant[:, 1].sum() != 1.0:
raise (RuntimeError,
"error in the MTOC quadrant detection for image %s" %
self._path)
return density_per_quadrant
def compute_cell_volume(self):
"""
compute cell volume in real pixel size using the cell mask
"""
cell_mask = self.get_cell_mask()
height_map = self.adjust_height_map()
cell_volume = height_map[cell_mask == 1].sum()
return cell_volume * helpers.volume_coeff()
def compute_peripheral_cell_volume(self):
"""
computes cell volume in in real pixel size using the cell mask
"""
peripheral_fraction_threshold = constants.analysis_config[
'PERIPHERAL_FRACTION_THRESHOLD']
cell_mask_dist_map = self.get_cell_mask_distance_map()
peripheral_binary_mask = (cell_mask_dist_map > 0) & \
(cell_mask_dist_map <= peripheral_fraction_threshold).astype(int)
cell_mask = self.get_cell_mask()
height_map = self.adjust_height_map()
height_map_periph = np.multiply(height_map, peripheral_binary_mask)
peripheral_cell_volume = height_map_periph[np.where(
cell_mask[:] == 1)].sum()
if (len(self.get_multiple_nucleus_centroid())) > 1:
return peripheral_cell_volume / len(
self.get_multiple_nucleus_centroid()) * helpers.volume_coeff()
else:
return peripheral_cell_volume * helpers.volume_coeff()
def compute_peripheral_cell_volume(self, peripheral_threshold):
"""
compute volume in the periphery in real pixel size using the cell mask
"""
cell_mask_dist_map = self.get_cell_mask_distance_map()
peripheral_binary_mask = (
(cell_mask_dist_map > 0) &
(cell_mask_dist_map <= peripheral_threshold)).astype(int)
height_map = self.adjust_height_map()
height_map_periph = np.multiply(height_map, peripheral_binary_mask)
peripheral_cell_volume = height_map_periph.sum(
) * helpers.volume_coeff()
return peripheral_cell_volume
def compute_cytoplasmic_volume(self):
"""compute volume of the cytoplasm in in real pixel size using cytoplasm mask and adjusted height map"""
cytoplasm_mask = self.get_cytoplasm_mask()
height_map = self.adjust_height_map()
cytoplasm_volume = height_map[cytoplasm_mask == 1].sum()
return cytoplasm_volume * helpers.volume_coeff()
def compute_nucleus_volume(self):
"""compute nucleus volume in in real pixel size using nucleus mask"""
nucleus_mask = self.get_nucleus_mask()
height_map = self.adjust_height_map()
nucleus_volume = height_map[nucleus_mask == 1].sum()
return nucleus_volume * helpers.volume_coeff()