def set_line_area(self, line_image, area_image, steps=20, verbose=False):
drawn_binary = np.zeros(self.shape)
if os.path.exists(line_image):
drawn_binary = read_tiff_stack(line_image)[0]['raw_image']
drawn_binary = make_binary_image_array(drawn_binary)
elif verbose:
sys.stderr.write(
"Skipping the invasive margin since there is none present.\n")
image_id1 = uuid4().hex
self._images[image_id1] = drawn_binary
area_binary = read_tiff_stack(area_image)[0]['raw_image']
area_binary = make_binary_image_array(area_binary)
image_id2 = uuid4().hex
self._images[image_id2] = area_binary
df = pd.DataFrame({
'custom_label': ['Drawn', 'Area'],
'image_id': [image_id1, image_id2]
})
df.index.name = 'db_id'
self.set_data('custom_images', df)
grown = binary_image_dilation(drawn_binary, steps=steps)
processed_image = self.get_image(self.processed_image_id).astype(
np.uint8)
margin_binary = grown & processed_image
tumor_binary = (area_binary & (~grown)) & processed_image
stroma_binary = (~(
(tumor_binary | grown) & processed_image)) & processed_image
d = {
'Margin': margin_binary,
'Tumor': tumor_binary,
'Stroma': stroma_binary
}
self.set_regions(d)
return d
def _read_seg_images(self, memb_seg_image_file, nuc_seg_image_file):
segmentation_names = []
memb = make_binary_image_array(
read_tiff_stack(memb_seg_image_file)[0]['raw_image'])
memb_id = uuid4().hex
self._images[memb_id] = memb.astype(int)
segmentation_names.append(['Membrane', memb_id])
nuc = read_tiff_stack(nuc_seg_image_file)[0]['raw_image']
nuc_id = uuid4().hex
self._images[nuc_id] = nuc.astype(int)
segmentation_names.append(['Nucleus', nuc_id])
#_mask_key = pd.DataFrame(mask_names,columns=['mask_label','image_id'])
#_mask_key.index.name = 'db_id'
#self.set_data('mask_images',_mask_key)
_segmentation_key = pd.DataFrame(
segmentation_names, columns=['segmentation_label', 'image_id'])
_segmentation_key.index.name = 'db_id'
self.set_data('segmentation_images', _segmentation_key)
def set_area(self,
area_image,
custom_mask_name,
other_mask_name,
verbose=False):
area_binary = read_tiff_stack(area_image)[0]['raw_image']
area_binary = make_binary_image_array(area_binary)
image_id = uuid4().hex
self._images[image_id] = area_binary
df = pd.DataFrame({'custom_label': ['Area'], 'image_id': [image_id]})
df.index.name = 'db_id'
self.set_data('custom_images', df)
processed_image = self.get_image(self.processed_image_id).astype(
np.uint8)
#margin_binary = grown&processed_image
tumor_binary = area_binary & processed_image
stroma_binary = (~(tumor_binary & processed_image)) & processed_image
d = {custom_mask_name: tumor_binary, other_mask_name: stroma_binary}
self.set_regions(d)
return d
def _read_seg_image(self, mibi_cell_labels_tif_path,
generate_processed_area_image,
processed_area_image_steps):
# Do our segmentation first
cell_labels = read_tiff_stack(
mibi_cell_labels_tif_path)[0]['raw_image']
image_id = uuid4().hex
self._images[image_id] = cell_labels
segmentation_names = [['Membrane', image_id]]
_segmentation_key = pd.DataFrame(
segmentation_names, columns=['segmentation_label', 'image_id'])
_segmentation_key.index.name = 'db_id'
self.set_data('segmentation_images', _segmentation_key)
# make the cell map to be a copy of the membrane segmentation
cell_map_id = uuid4().hex
self._images[cell_map_id] = cell_labels.copy()
increment = self.get_data('segmentation_images').index.max() + 1
extra = pd.DataFrame(
pd.Series(
dict({
'db_id': increment,
'segmentation_label': 'cell_map',
'image_id': cell_map_id
}))).T
extra = pd.concat(
[self.get_data('segmentation_images'),
extra.set_index('db_id')])
self.set_data('segmentation_images', extra)
# make the edge map incrementally
edge_map_id = uuid4().hex
self._images[edge_map_id] = image_edges(cell_labels)
increment = self.get_data('segmentation_images').index.max() + 1
extra = pd.DataFrame(
pd.Series(
dict({
'db_id': increment,
'segmentation_label': 'edge_map',
'image_id': edge_map_id
}))).T
extra = pd.concat(
[self.get_data('segmentation_images'),
extra.set_index('db_id')])
self.set_data('segmentation_images', extra)
image_id = uuid4().hex
mask_names = [['ProcessRegionImage', image_id]]
processed = make_binary_image_array(np.ones(cell_labels.shape))
self._images[image_id] = processed
if self.verbose:
sys.stderr.write("size of image: " + str(processed.sum().sum()) +
"\n")
if generate_processed_area_image:
if self.verbose:
sys.stderr.write(
"Creating approximate processed_image_area by watershed.\n"
)
processed = binary_image_dilation(
make_binary_image_array(cell_labels),
steps=processed_area_image_steps)
self._images[image_id] = processed
_mask_key = pd.DataFrame(mask_names,
columns=['mask_label', 'image_id'])
_mask_key.index.name = 'db_id'
self.set_data('mask_images', _mask_key)
self.set_processed_image_id(image_id)
self._images[self.processed_image_id] = processed.astype(np.int8)
if self.verbose:
sys.stderr.write("size of processed image: " +
str(processed.sum().sum()) + "\n")