prepare low-res tadpole images for template generation

brainglobe_template_builder/preproc/alignment.py

@@ -183,7 +183,7 @@ def _compute_transform(self):
             translation_to_mid_axis @ rotation @ translation_to_origin
         )
 
-    def transform_image(self, image: np.ndarray = None):
+    def transform_image(self, image: np.ndarray | None = None):
         """Transform the image using the transformation matrix.
 
         Parameters

brainglobe_template_builder/preproc/cropping.py

@@ -2,7 +2,7 @@
 
 
 def crop_to_mask(
-    stack: np.ndarray, mask: np.ndarray, padding: np.uint8 = 0
+    stack: np.ndarray, mask: np.ndarray, padding: np.uint8 = np.uint8(0)
 ) -> tuple[np.ndarray, np.ndarray]:
     """
     Crop stack and mask to the mask extent, and pad with zeros.

brainglobe_template_builder/preproc/masking.py

@@ -1,4 +1,4 @@
-from typing import Literal, Union
+from typing import Callable, Literal, Union
 
 import numpy as np
 from skimage import filters, measure, morphology
@@ -44,7 +44,7 @@ def _threshold_image(
         A binary mask.
     """
 
-    method_to_func = {
+    method_to_func: dict[str, Callable] = {
         "triangle": filters.threshold_triangle,
         "otsu": filters.threshold_otsu,
         "isodata": filters.threshold_isodata,

brainglobe_template_builder/preproc/transform_utils.py

@@ -118,7 +118,7 @@ def downsample_anisotropic_image_stack(
 
     # we have xy slices as chunks, so apply downscaling in xy first
     downsampled_inplane = stack.map_blocks(
-        transform.downscale_local_mean,
+        transform.downscale_local_mean,  # type: ignore
         (1, in_plane_factor, in_plane_factor),
         dtype=np.float64,
     )

examples/tadpole/2_prep_lowres.py

@@ -0,0 +1,267 @@
+"""
+Prepare low-resolution tadpole images for template construction
+================================================================
+The ``brainglobe-template-builder`` Preprocess widget was used to perform
+the following operations on each subject's low-resolution image, ahead of
+running this script:
+- Load each image from tiff, re-orient to ASR and save as nifti
+- Generate a brain mask automatically and modify it manually, if necessary
+- Find the mid-sagittal plane and align the image and mask accordingly
+- Save the aligned image, brain mask, and halves mask as nifti files
+
+This script will:
+- Perform N4 Bias field correction on the aligned image using ANTs
+- Split the image and mask into hemispheres and reflect each hemisphere
+- Generate symmetric brains using either the left or right hemisphere
+- Save all resulting images as nifti files to be used for template construction
+"""
+
+# %%
+# Imports
+# -------
+import os
+from datetime import date
+from pathlib import Path
+
+import ants
+import numpy as np
+import pandas as pd
+from loguru import logger
+from tqdm import tqdm
+
+from brainglobe_template_builder.io import (
+    file_path_with_suffix,
+)
+from brainglobe_template_builder.preproc.splitting import (
+    generate_arrays_4template,
+    save_array_dict_to_nii,
+)
+
+# %%
+# Setup
+# -----
+# Define some global variables, including paths to input and output directories
+# and set up logging.
+
+# Define voxel size(in microns) of the lowest resolution image
+lowres = 12
+# String to identify the resolution in filenames
+res_str = f"res-{lowres}um"
+# Define voxel sizes in mm (for Nifti saving)
+vox_sizes = [lowres * 1e-3] * 3  # in mm
+
+# Masking parameters
+mask_params = {
+    "gauss_sigma": 2,
+    "threshold_method": "triangle",
+    "closing_size": 3,
+}
+
+# Prepare directory structure
+atlas_forge_dir = Path("/media/ceph-niu/neuroinformatics/atlas-forge")
+project_dir = (
+    atlas_forge_dir / "Tadpole" / "tadpole-template-starter" / "old-tadpole"
+)
+raw_dir = project_dir / "rawdata"
+deriv_dir = project_dir / "derivatives"
+assert deriv_dir.exists(), f"Could not find derivatives directory {deriv_dir}."
+
+# Set up logging
+today = date.today()
+current_script_name = os.path.basename(__file__).replace(".py", "")
+logger.add(project_dir / "logs" / f"{today}_{current_script_name}.log")
+
+# %%
+# Load a dataframe with image paths to use for the template
+# ---------------------------------------------------------
+
+source_csv_dir = project_dir / "templates" / "use_for_template.csv"
+df = pd.read_csv(source_csv_dir)
+
+# take the hemi column and split it into two boolean columns
+# named use_left and use_right (for easier filtering)
+df["use_left"] = df["hemi"].isin(["left", "both"])
+df["use_right"] = df["hemi"].isin(["right", "both"])
+# Keep only the rows in which at least one of use_left or use_right is True
+df = df[df[["use_left", "use_right"]].any(axis=1)]
+n_subjects = len(df)
+
+# %%
+# Run the pipeline for each subject
+# ---------------------------------
+
+# Create a dictionary to store the paths to the use4template directories
+# per subject. These will contain all necessary images for template building.
+use4template_dirs = {}
+
+for idx, row in tqdm(df.iterrows(), total=n_subjects):
+    # Figure out input-output paths
+    row = df.iloc[idx]
+    subject = "sub-" + row["subject_id"]
+    channel_str = "channel-" + row["color"]
+    source_origin = row["orientation"]
+    file_prefix = f"{subject}_{res_str}_{channel_str}"
+    deriv_subj_dir = deriv_dir / subject
+    deriv_subj_dir.mkdir(exist_ok=True)
+    tiff_path = raw_dir / f"{file_prefix}.tif"  # original tiff image
+    nii_ext = ".nii.gz"  # extension for nifti files
+
+    logger.info(f"Starting to process {file_prefix}...")
+    logger.info(f"Will save outputs to {deriv_subj_dir}/")
+
+    # Load the manually aligned nifti image
+    nii_path = file_path_with_suffix(
+        deriv_subj_dir / tiff_path.name, "_orig-asr_aligned", new_ext=nii_ext
+    )
+    image_ants = ants.image_read(nii_path.as_posix())
+    logger.debug(
+        f"Read image with shape: {image_ants.shape} from {nii_path.name}."
+    )
+
+    # Load the manually adjusted brain mask for the image
+    mask_path = file_path_with_suffix(
+        deriv_subj_dir / tiff_path.name,
+        "_orig-asr_label-brain_aligned",
+        new_ext=nii_ext,
+    )
+    mask = ants.image_read(mask_path.as_posix())
+    logger.debug(
+        f"Read brain mask with shape: {mask.shape} from {mask_path.name}."
+    )
+
+    # Load the halves mask for the image (also pre-generated via the widget)
+    # This will be later used for diagnostic plotting
+    halves_mask_path = file_path_with_suffix(
+        deriv_subj_dir / tiff_path.name,
+        "_orig-asr_label-halves_aligned",
+        new_ext=nii_ext,
+    )
+    halves_mask = ants.image_read(halves_mask_path.as_posix())
+    logger.debug(
+        f"Read halves mask with shape: {halves_mask.shape} from "
+        f"{halves_mask_path.name}."
+    )
+
+    # Bias field correction (to homogenise intensities)
+    image_n4 = ants.n4_bias_field_correction(image_ants)
+    image_n4_masked_numpy = image_n4.numpy() * mask.numpy()
+    image_n4_path = file_path_with_suffix(nii_path, "_N4")
+    image_n4 = image_n4.new_image_like(image_n4_masked_numpy)
+    ants.image_write(image_n4, image_n4_path.as_posix())
+    logger.debug(
+        f"Created N4 bias field corrected image as {image_n4_path.name}."
+    )
+
+    # Plot the mask over the image to check
+    mask_plot_path = (
+        deriv_subj_dir / f"{file_prefix}_orig-asr_N4_mask-overlay.png"
+    )
+    ants.plot(
+        image_n4,
+        mask,
+        overlay_alpha=0.5,
+        axis=1,
+        title="Brain mask over image",
+        filename=mask_plot_path.as_posix(),
+    )
+    logger.debug("Plotted overlay to visually check mask.")
+
+    # Plot the halves mask over the aligned image to check the split
+    output_prefix = file_path_with_suffix(nii_path, "_N4_aligned_", new_ext="")
+    ants.plot(
+        image_n4,
+        halves_mask,
+        overlay_alpha=0.5,
+        axis=1,
+        title="Aligned image split into right and left halves",
+        filename=output_prefix.as_posix() + "halves-overlay.png",
+    )
+    logger.debug("Plotted overlays to visually check alignment.")
+
+    # Generate arrays for template construction and save as niftis
+    use4template_dir = Path(output_prefix.as_posix() + "padded_use4template")
+    # if it exists, delete existing files in it
+    if use4template_dir.exists():
+        logger.warning(f"Removing existing files in {use4template_dir}.")
+        for file in use4template_dir.glob("*"):
+            file.unlink()
+    use4template_dir.mkdir(exist_ok=True)
+
+    array_dict = generate_arrays_4template(
+        subject, image_n4.numpy(), mask.numpy(), pad=2
+    )
+    save_array_dict_to_nii(array_dict, use4template_dir, vox_sizes)
+    use4template_dirs[subject] = use4template_dir
+    logger.info(
+        f"Saved images for template construction in {use4template_dir}."
+    )
+    logger.info(f"Finished processing {file_prefix}.")
+
+
+# %%
+# Generate lists of file paths for template construction
+# -----------------------------------------------------
+# Use the paths to the use4template directories to generate lists of file paths
+# for the template construction pipeline. Three kinds of template will be
+# generated, and each needs the corresponging brain image and mask files:
+# 1. All asym* files for subjects where hemi=both. These will be used to
+#    generate an asymmetric brain template.
+# 2. All right-sym* files for subjects where use_right is True, and
+#    all left-sym* files for subjects where use_left is True.
+#    These will be used to generate a symmetric brain template.
+# 3. All right-hemi* files for subjects where use_right is True,
+#    and all left-hemi-xflip* files for subjects where use_left is True.
+#    These will be used to generate a symmetric hemisphere template.
+
+filepath_lists: dict[str, list] = {
+    "asym-brain": [],
+    "asym-mask": [],
+    "sym-brain": [],
+    "sym-mask": [],
+    "hemi-brain": [],
+    "hemi-mask": [],
+}
+
+for _, row in df.iterrows():
+    subject = "sub-" + row["subject_id"]
+    use4template_dir = use4template_dirs[subject]
+
+    if row["hemi"] == "both":
+        # Add paths for the asymmetric brain template
+        for label in ["brain", "mask"]:
+            filepath_lists[f"asym-{label}"].append(
+                use4template_dir / f"{subject}_asym-{label}.nii.gz"
+            )
+
+    if row["use_right"]:
+        for label in ["brain", "mask"]:
+            # Add paths for the symmetric brain template
+            filepath_lists[f"sym-{label}"].append(
+                use4template_dir / f"{subject}_right-sym-{label}.nii.gz"
+            )
+            # Add paths for the hemispheric template
+            filepath_lists[f"hemi-{label}"].append(
+                use4template_dir / f"{subject}_right-hemi-{label}.nii.gz"
+            )
+
+    if row["use_left"]:
+        for label in ["brain", "mask"]:
+            # Add paths for the symmetric brain template
+            filepath_lists[f"sym-{label}"].append(
+                use4template_dir / f"{subject}_left-sym-{label}.nii.gz"
+            )
+            # Add paths for the hemispheric template
+            filepath_lists[f"hemi-{label}"].append(
+                use4template_dir / f"{subject}_left-hemi-xflip-{label}.nii.gz"
+            )
+
+# %%
+# Save the file paths to text files, each in a separate directory
+
+for key, paths in filepath_lists.items():
+    kind, label = key.split("-")  # e.g. "asym" and "brain"
+    n_images = len(paths)
+    template_name = f"template_{kind}_{res_str}_n-{n_images}"
+    template_dir = project_dir / "templates" / template_name
+    template_dir.mkdir(exist_ok=True)
+    np.savetxt(template_dir / f"{label}_paths.txt", paths, fmt="%s")