Script to generate Allen mouse brain atlas with barrel annotations

brainglobe_atlasapi/atlas_generation/atlas_scripts/allen_mouse_bluebrain_barrels.py

@@ -0,0 +1,344 @@
+__version__ = "0"
+import json
+import time
+from pathlib import Path
+
+import nrrd
+import numpy as np
+import pooch
+from allensdk.api.queries.ontologies_api import OntologiesApi
+from allensdk.api.queries.reference_space_api import ReferenceSpaceApi
+from allensdk.core.reference_space_cache import ReferenceSpaceCache
+from requests import exceptions
+from rich.progress import track
+from tqdm import tqdm
+
+from brainglobe_atlasapi import descriptors, utils
+from brainglobe_atlasapi.atlas_generation.mesh_utils import (
+    Region,
+    create_region_mesh,
+)
+from brainglobe_atlasapi.atlas_generation.wrapup import wrapup_atlas_from_data
+from brainglobe_atlasapi.structure_tree_util import get_structures_tree
+
+
+def create_atlas(working_dir, resolution):
+    # Specify information about the atlas:
+    ATLAS_NAME = "allen_mouse_bluebrain_barrels"
+    SPECIES = "Mus musculus"
+    ATLAS_LINK = "http://www.brain-map.org"
+    CITATION = "Bolaños-Puchet S., Teska A., et al. (2024).  https://doi.org/10.1162/imag_a_00209"
+    ATLAS_PACKAGER = "Axel Bisi"
+    ORIENTATION = "asr"
+
+    # Temporary folder for nrrd files download:
+    download_dir_path = working_dir / "downloading_path"
+    download_dir_path.mkdir(exist_ok=True)
+
+    # Download original Allen template volume:
+    #########################################
+    spacecache = ReferenceSpaceCache(
+        manifest=download_dir_path / "manifest.json",
+        # downloaded files are stored relative to here
+        resolution=resolution,
+        reference_space_key="annotation/ccf_2017",
+        # use the latest version of the CCF
+    )
+
+    template_volume, _ = spacecache.get_template_volume()
+
+    # Download enhanced barrel-containing Allen annotation files by BlueBrain,
+    # and hierarchy:
+    #########################################
+    annotation_dir_path = working_dir / "downloading_path/annotation_enhanced"
+    annotation_dir_path.mkdir(exist_ok=True)
+
+    if resolution == 10:
+        gin_url = "https://gin.g-node.org/BrainGlobe/bluebrain_barrel_materials/raw/master/annotation_barrels_10.nrrd"
+    elif resolution == 25:
+        gin_url = "https://gin.g-node.org/BrainGlobe/bluebrain_barrel_materials/raw/master/annotation_barrels_25.nrrd"
+    else:
+        raise ValueError("Resolution {}um not supported.".format(resolution))
+
+    utils.check_internet_connection()
+    annotation_file_path = pooch.retrieve(
+        gin_url,
+        known_hash=None,
+        path=annotation_dir_path,
+        progressbar=True,
+    )
+
+    # Load enhanced annotation volume:
+    annotated_volume = nrrd.read(annotation_file_path)[0]
+
+    # Download structures tree and meshes:
+    ######################################
+    oapi = OntologiesApi()  # ontologies
+    struct_tree = spacecache.get_structure_tree()  # structures tree
+
+    # Find id of set of regions with mesh:
+    select_set = (
+        "Structures whose surfaces are represented by a precomputed mesh"
+    )
+
+    mesh_set_ids = [
+        s["id"]
+        for s in oapi.get_structure_sets()
+        if s["description"] == select_set
+    ]
+
+    # Get structures with mesh for both versions
+    structs_with_mesh = struct_tree.get_structures_by_set_id(mesh_set_ids)
+
+    # Download hierarchy:
+    gin_url = "https://gin.g-node.org/BrainGlobe/bluebrain_barrel_materials/raw/master/hierarchy.json"
+    hierarchy_path = pooch.retrieve(
+        gin_url,
+        known_hash=None,
+        path=annotation_dir_path,
+        fname="hierarchy.json",
+        progressbar=True,
+    )
+    structs_with_barrels = json.load(open(hierarchy_path))
+
+    # Add barrels structures to Allen structures
+    def find_dicts_with_key_containing_substring(d, key, substring):
+        """
+        Recursively find all dictionaries within a nested dictionary that
+        contain a specific substring in the value associated with a given key.
+
+        Args:
+        d (dict): The input dictionary.
+        key (str): The key to search for.
+        substring (str): The substring to search for in the value associated
+        with the key.
+
+        Returns:
+        list: A list of dictionaries that contain the key with a value
+        containing the substring.
+        """
+        if not isinstance(d, dict):
+            raise ValueError("Input should be a dictionary")
+
+        matching_dicts = []
+
+        def recurse(sub_d):
+            contains_substring = False
+
+            for k, v in sub_d.items():
+                if isinstance(v, dict):
+                    recurse(v)
+                elif isinstance(v, list):
+                    for item in v:
+                        if isinstance(item, dict):
+                            recurse(item)
+                if k == key and substring in str(v):
+                    contains_substring = True
+
+            if contains_substring:
+                matching_dicts.append(sub_d)
+
+        recurse(d)
+
+        return matching_dicts
+
+    matching_dicts = find_dicts_with_key_containing_substring(
+        structs_with_barrels, key="name", substring="barrel"
+    )
+    matching_dicts = [
+        d
+        for d in matching_dicts
+        if d["graph_order"] in [52, 53, 54, 55, 56, 57]
+    ]
+    main_barrel_parent_struct = [
+        s for s in structs_with_mesh if s["acronym"] == "SSp-bfd"
+    ][0]
+    structures_present = [
+        "SSp-bfd1",
+        "SSp-bfd2/3",
+        "SSp-bfd4",
+        "SSp-bfd5",
+        "SSp-bfd6a",
+        "SSp-bfd6b",
+    ]  # keep laminar structures
+    keys_to_keep = [
+        "acronym",
+        "graph_id",
+        "graph_order",
+        "id",
+        "name",
+        "rgb_triplet",
+        "structure_set_ids",
+        "structure_id_path",
+    ]
+    dict_to_add = []
+    for d in matching_dicts:
+
+        # Ignore parent-level SSp-bfd layers
+        if d["acronym"] in structures_present:
+            print("Skipping", d, "already present.")
+            continue
+        # Ignore sub-structures layer 2 and 3 to keep layer 2/3 structure
+        if d["graph_order"] == 53 and d["acronym"] in ["SSp-bfd2", "SSp-bfd3"]:
+            print("Excluding", d, "to keep layer 2/3 structure only.")
+            continue
+        elif d["graph_order"] == 54 and (
+            "layer 2" in d["name"] or "layer 3" in d["name"]
+        ):
+            print("Excluding", d, "to keep layer 2/3 structure only.")
+            continue
+
+        # Add desired barrel-related structures, with corresponding fields
+        else:
+            current_id = d["id"]
+            # Find corresponding parent structure
+            if d["graph_order"] == 52:  # barrel-level -> find SSp-bfd
+                # Create new structure_id_path for barrel structure
+                d["structure_id_path"] = main_barrel_parent_struct[
+                    "structure_id_path"
+                ] + [current_id]
+            elif (
+                d["graph_order"] == 53
+            ):  # barrel layer-level -> find SSp-bfd-barrel also
+                parent_struct_id = d["parent_structure_id"]
+                parent_struct = [
+                    s for s in matching_dicts if s["id"] == parent_struct_id
+                ][0]
+                parent_struct["structure_id_path"] = main_barrel_parent_struct[
+                    "structure_id_path"
+                ] + [parent_struct_id]
+                # Create new structure_id_path for barrel-layer structure
+                d["structure_id_path"] = (
+                    main_barrel_parent_struct["structure_id_path"]
+                    + [d["parent_structure_id"]]
+                    + [current_id]
+                )
+
+            # Complete with other keys
+            d["rgb_triplet"] = main_barrel_parent_struct["rgb_triplet"]
+            d["graph_id"] = 1
+            d["structure_set_ids"] = None
+            dict_to_add.append({k: d[k] for k in keys_to_keep})
+
+    # Add list of dicts to structs_with_mesh
+    structs_with_mesh = structs_with_mesh + dict_to_add
+    print("Added the following structures to the atlas:")
+    for d in dict_to_add:
+        print(d["name"])
+
+    # Directory for mesh saving:
+    meshes_dir = working_dir / descriptors.MESHES_DIRNAME
+
+    # Download existing Allen meshes:
+    space = ReferenceSpaceApi()
+    meshes_dict = dict()
+    for s in tqdm(structs_with_mesh):
+        name = s["id"]
+        filename = meshes_dir / f"{name}.obj"
+        try:
+            space.download_structure_mesh(
+                structure_id=s["id"],
+                ccf_version="annotation/ccf_2017",
+                file_name=filename,
+            )
+            meshes_dict[name] = filename
+        except (exceptions.HTTPError, ConnectionError):
+            print(f"Failed to download mesh for {s['name']} ({s['id']})")
+
+    # Create missing meshes
+    tree = get_structures_tree(structs_with_mesh)
+    print(
+        f"Number of brain regions: {tree.size()}, "
+        f"max tree depth: {tree.depth()}"
+    )
+
+    # generate binary mask for mesh creation
+    labels = np.unique(annotated_volume).astype(np.int_)
+    for key, node in tree.nodes.items():
+        if key in labels:
+            is_label = True
+        else:
+            is_label = False
+
+        node.data = Region(is_label)
+
+    start = time.time()
+
+    root_id = 997
+    closing_n_iters = 2
+    decimate_fraction = 0.3
+    smooth = True
+
+    for node in track(
+        tree.nodes.values(),
+        total=tree.size(),
+        description="Creating meshes",
+    ):
+        # Check if mesh already exists
+        filename = meshes_dir / f"{node.identifier}.obj"
+        if filename.exists():
+            continue
+
+        create_region_mesh(
+            (
+                meshes_dir,
+                node,
+                tree,
+                labels,
+                annotated_volume,
+                root_id,
+                closing_n_iters,
+                decimate_fraction,
+                smooth,
+            )
+        )
+
+        meshes_dict[node.identifier] = filename
+
+    print(
+        "Finished mesh extraction in : ",
+        round((time.time() - start) / 60, 2),
+        " minutes",
+    )
+
+    # Loop over structures, remove entries not used:
+    for struct in structs_with_mesh:
+        [
+            struct.pop(k)
+            for k in ["graph_id", "structure_set_ids", "graph_order"]
+        ]
+
+    # Wrap up, compress, and remove file:0
+    print("Finalising atlas")
+    output_filename = wrapup_atlas_from_data(
+        atlas_name=ATLAS_NAME,
+        atlas_minor_version=__version__,
+        citation=CITATION,
+        atlas_link=ATLAS_LINK,
+        species=SPECIES,
+        resolution=(resolution,) * 3,
+        orientation=ORIENTATION,
+        root_id=997,
+        reference_stack=template_volume,
+        annotation_stack=annotated_volume,
+        structures_list=structs_with_mesh,
+        meshes_dict=meshes_dict,
+        working_dir=working_dir,
+        hemispheres_stack=None,
+        cleanup_files=False,
+        compress=True,
+        additional_metadata={"atlas_packager": ATLAS_PACKAGER},
+    )
+
+    return output_filename
+
+
+if __name__ == "__main__":
+    RES_UM = 10
+    # Generated atlas path:
+    bg_root_dir = (
+        Path.home() / "brainglobe_workingdir" / "allen_mouse_bluebrain_barrels"
+    )
+    bg_root_dir.mkdir(exist_ok=True)
+
+    create_atlas(bg_root_dir, RES_UM)

pyproject.toml

@@ -53,6 +53,10 @@ dev = [
     "tox",
 ]
 allenmouse = ["allensdk"]
+allenmouse_barrels = [
+    "allensdk",
+    "voxcell"]
+
 atlasgen = [
     "loguru",
     "PyMCubes",