def test_atlas_value_to_structure_id():
structure_df = load_structures_as_df(structures_csv)
assert structure_id_100 == structures_tree.atlas_value_to_structure_id(
100, structure_df)
with pytest.raises(structures_tree.UnknownAtlasValue):
structures_tree.atlas_value_to_structure_id(100000, structure_df)
def test_atlas_value_to_name():
structure_df = load_structures_as_df(structures_csv)
assert "Interpeduncular nucleus" == structures_tree.atlas_value_to_name(
100, structure_df)
with pytest.raises(structures_tree.UnknownAtlasValue):
structures_tree.atlas_value_to_name(100000, structure_df)
def save_analyse_regions(viewer):
ensure_directory_exists(paths.regions_directory)
delete_directory_contents(str(paths.regions_directory))
if volumes:
annotations = load_any(paths.annotations)
hemispheres = load_any(paths.hemispheres)
structures_reference_df = load_structures_as_df(
get_structures_path())
print(
f"\nSaving summary volumes to: {paths.regions_directory}")
for label_layer in label_layers:
analyse_region_brain_areas(
label_layer,
paths.regions_directory,
annotations,
hemispheres,
structures_reference_df,
)
print(f"\nSaving regions to: {paths.regions_directory}")
for label_layer in label_layers:
save_regions_to_file(
label_layer,
paths.regions_directory,
paths.downsampled_image,
)
close_viewer(viewer)
def main():
print("Starting amap viewer")
args = parser().parse_args()
structures_df = load_structures_as_df(get_structures_path())
if not args.memory:
print(
"By default amap_vis does not load data into memory. "
"To speed up visualisation, use the '-m' flag. Be aware "
"this will make the viewer slower to open initially."
)
paths = Paths(args.amap_directory)
with napari.gui_qt():
v = napari.Viewer(title="amap viewer")
if (
Path(paths.registered_atlas_path).exists()
and Path(paths.boundaries_file_path).exists()
):
if args.raw:
image_scales = display_raw(v, args)
else:
if Path(paths.downsampled_brain_path).exists():
image_scales = display_downsampled(v, args, paths)
else:
raise FileNotFoundError(
f"The downsampled image: "
f"{paths.downsampled_brain_path} could not be found. "
f"Please ensure this is the correct "
f"directory and that amap has completed. "
)
region_labels = display_registration(
v,
paths.registered_atlas_path,
paths.boundaries_file_path,
image_scales,
memory=args.memory,
)
@region_labels.mouse_move_callbacks.append
def display_region_name(layer, event):
display_brain_region_name(layer, structures_df)
else:
raise FileNotFoundError(
f"The directory: '{args.amap_directory}' does not "
f"appear to be complete. Please ensure this is the correct "
f"directory and that amap has completed."
)
def test_load_structures_df():
structures = load_structures_as_df(structures_csv)
assert len(structures) == 1299
assert (structures.keys() == Index(structure_headers)).all()
structures_test = list(structures.iloc[100].array)
assert structures_test[1] == structures_line_100[1]
assert structures_test[2] == structures_line_100[2]
assert structures_test[3] == structures_line_100[3]
def analysis_run(args, file_name="summary_cell_counts.csv"):
args = prep_atlas_conf(args)
atlas = brainio.load_any(args.paths.registered_atlas_path)
hemisphere = brainio.load_any(args.paths.hemispheres_atlas_path)
cells = get_cells_data(
args.paths.classification_out_file,
cells_only=args.cells_only,
)
max_coords = get_max_coords(cells) # Useful for debugging dimensions
structures_reference_df = load_structures_as_df(get_structures_path())
atlas_pixel_sizes = get_atlas_pixel_sizes(args.atlas_config)
sample_pixel_sizes = args.x_pixel_um, args.y_pixel_um, args.z_pixel_um
scales = get_scales(sample_pixel_sizes, atlas_pixel_sizes,
args.scale_cell_coordinates)
structures_with_cells = set()
for i, cell in enumerate(tqdm(cells)):
transform_cell_coords(atlas, cell, scales)
structure_id = get_structure_from_coordinates(
atlas,
cell,
max_coords,
order=args.coordinates_order,
structures_reference_df=structures_reference_df,
)
if structure_id is not None:
cell.structure_id = structure_id
structures_with_cells.add(structure_id)
else:
continue
cell.hemisphere = get_structure_from_coordinates(
hemisphere, cell, max_coords, order=args.coordinates_order)
sorted_cell_numbers = get_cells_nbs_df(cells, structures_reference_df,
structures_with_cells)
combined_hemispheres = combine_df_hemispheres(sorted_cell_numbers)
df = calculate_densities(combined_hemispheres, args.paths.volume_csv_path)
df = sanitise_df(df)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
output_file = os.path.join(args.output_dir, file_name)
df.to_csv(output_file, index=False)
def calculate_volumes(
atlas_path,
hemispheres_path,
atlas_structures_path,
registration_config,
output_file,
left_hemisphere_value=2,
right_hemisphere_value=1,
):
(
unique_vals_left,
unique_vals_right,
counts_left,
counts_right,
) = get_lateralised_atlas(
atlas_path,
hemispheres_path,
left_hemisphere_value=left_hemisphere_value,
right_hemisphere_value=right_hemisphere_value,
)
structures_reference_df = load_structures_as_df(atlas_structures_path)
voxel_volume = get_voxel_volume(registration_config)
voxel_volume_in_mm = voxel_volume / (1000**3)
df = initialise_df(
"structure_name",
"left_volume_mm3",
"right_volume_mm3",
"total_volume_mm3",
)
for atlas_value in unique_vals_left:
if atlas_value is not 0: # outside brain
try:
df = add_structure_volume_to_df(
df,
atlas_value,
structures_reference_df,
unique_vals_left,
unique_vals_right,
counts_left,
counts_right,
voxel_volume_in_mm,
)
except UnknownAtlasValue:
print(
"Value: {} is not in the atlas structure reference file. "
"Not calculating the volume".format(atlas_value))
df.to_csv(output_file, index=False)
def load_registration(self):
self.status_label.setText("Loading...")
self.region_labels = display_registration(
self.viewer,
self.registration_paths.registered_atlas_path,
self.registration_paths.boundaries_file_path,
self.image_scales,
memory=memory,
)
self.structures_df = load_structures_as_df(get_structures_path())
self.status_label.setText("Ready")
@self.region_labels.mouse_move_callbacks.append
def display_region_name(layer, event):
display_brain_region_name(layer, self.structures_df)
def load_amap_directory(self):
self.select_nii_file()
self.registration_directory = self.downsampled_file.parent
self.status_label.setText(f"Loading ...")
self.paths = Paths(self.registration_directory, self.downsampled_file)
if not self.paths.tmp__inverse_transformed_image.exists():
print(
f"The image: '{self.downsampled_file}' has not been transformed into standard "
f"space, and so must be transformed before segmentation.\n")
transform_image_to_standard_space(
self.registration_directory,
image_to_transform_fname=self.downsampled_file,
output_fname=self.paths.tmp__inverse_transformed_image,
log_file_path=self.paths.tmp__inverse_transform_log_path,
error_file_path=self.paths.tmp__inverse_transform_error_path,
)
else:
print("Registered image exists, skipping registration\n")
self.registered_image = prepare_load_nii(
self.paths.tmp__inverse_transformed_image, memory=memory)
self.base_layer = display_channel(
self.viewer,
self.registration_directory,
self.paths.tmp__inverse_transformed_image,
memory=memory,
name="Image in standard space",
)
self.initialise_image_view()
self.structures_df = load_structures_as_df(get_structures_path())
self.load_button.setMinimumWidth(0)
self.load_atlas_button.setVisible(True)
self.save_button.setVisible(True)
self.initialise_region_segmentation()
self.initialise_track_tracing()
self.status_label.setText(f"Ready")
def xml_crop(args, df_query="name"):
args = prep_atlas_conf(args)
if args.structures_file_path is None:
args.structures_file_path = get_structures_path()
reference_struct_df = pd.read_csv(get_structures_path())
curate_struct_df = pd.read_csv(args.structures_file_path)
curate_struct_df = reference_struct_df[
reference_struct_df[df_query].isin(curate_struct_df[df_query])
]
curated_ids = list(curate_struct_df["structure_id_path"])
atlas = brainio.load_any(args.registered_atlas_path)
hemisphere = brainio.load_any(args.hemispheres_atlas_path)
structures_reference_df = load_structures_as_df(get_structures_path())
atlas_pixel_sizes = get_atlas_pixel_sizes(args.atlas_config)
sample_pixel_sizes = args.x_pixel_um, args.y_pixel_um, args.z_pixel_um
scales = cells_regions.get_scales(sample_pixel_sizes, atlas_pixel_sizes)
destination_folder = os.path.join(args.xml_dir, "xml_crop")
if not os.path.exists(destination_folder):
os.makedirs(destination_folder)
xml_names = [f for f in os.listdir(args.xml_dir) if f.endswith(".xml")]
xml_paths = [os.path.join(args.xml_dir, f) for f in xml_names]
for idx, xml_path in enumerate(xml_paths):
print("Curating file: {}".format(xml_names[idx]))
cells = cells_regions.get_cells_data(
xml_path, cells_only=args.cells_only,
)
max_coords = cells_regions.get_max_coords(cells)
curated_cells = []
for i, cell in enumerate(cells):
cells_regions.transform_cell_coords(atlas, cell, scales)
structure_id = cells_regions.get_structure_from_coordinates(
atlas,
cell,
max_coords,
order=args.coordinates_order,
structures_reference_df=structures_reference_df,
)
if structure_id in curated_ids:
if args.hemisphere_query in [1, 2]:
hemisphere = cells_regions.get_structure_from_coordinates(
hemisphere,
cell,
max_coords,
order=args.coordinates_order,
)
if hemisphere is args.hemisphere_query:
curated_cells.append(cell)
else:
curated_cells.append(cell)
cells_to_xml(
curated_cells,
os.path.join(destination_folder, xml_names[idx]),
artifact_keep=True,
)
print("Done!")
from neuro.atlas_tools import paths as reg_paths
from neuro.atlas_tools.misc import get_voxel_volume, get_atlas_pixel_sizes
import napari
import numpy as np
amap_output_dir = (
"/media/adam/Storage/cellfinder/analysis/inj_segment/CT_CX_142_2_test")
region_acronym = "RSP"
visual_check = False
left_hemisphere_value = 2
right_hemisphere_value = 1
properties_to_fetch = ["area", "bbox", "centroid"]
structures_reference_df = load_structures_as_df(get_structures_path())
region_value = int(structures_reference_df[structures_reference_df["acronym"]
== region_acronym]["id"])
region_search_string = "/" + str(region_value) + "/"
sub_regions = structures_reference_df[structures_reference_df[
"structure_id_path"].str.contains(region_search_string)]
amap_output_dir = Path(amap_output_dir)
annotations_image = load_any(amap_output_dir / reg_paths.ANNOTATIONS)
midpoint = int(annotations_image.shape[0] // 2)
hemispheres_image = load_any(amap_output_dir / reg_paths.HEMISPHERES)
sub_region_values = list(sub_regions["id"])
def main():
print("Starting amap viewer")
args = parser().parse_args()
structures_path = get_structures_path()
structures_df = load_structures_as_df(structures_path)
if not args.memory:
print(
"By default amap_vis does not load data into memory. "
"To speed up visualisation, use the '-m' flag. Be aware "
"this will make the viewer slower to open initially."
)
paths = Paths(args.amap_directory)
with napari.gui_qt():
v = napari.Viewer(title="amap viewer")
if (
Path(paths.registered_atlas_path).exists()
and Path(paths.boundaries_file_path).exists()
):
if args.raw:
image_scales = display_raw(v, args)
else:
if Path(paths.downsampled_brain_path).exists():
image_scales = display_downsampled(v, args, paths)
else:
raise FileNotFoundError(
f"The downsampled image: "
f"{paths.downsampled_brain_path} could not be found. "
f"Please ensure this is the correct "
f"directory and that amap has completed. "
)
labels = display_registration(
v,
paths.registered_atlas_path,
paths.boundaries_file_path,
image_scales,
memory=args.memory,
)
@labels.mouse_move_callbacks.append
def get_connected_component_shape(layer, event):
val = layer.get_value()
if val != 0 and val is not None:
try:
region = atlas_value_to_name(val, structures_df)
msg = f"{region}"
except UnknownAtlasValue:
msg = "Unknown region"
else:
msg = "No label here!"
layer.help = msg
else:
raise FileNotFoundError(
f"The directory: '{args.amap_directory}' does not "
f"appear to be complete. Please ensure this is the correct "
f"directory and that amap has completed."
)