Update annotation comparison

flamingo_tools/segmentation/synapse_detection.py

@@ -0,0 +1,220 @@
+import os
+from typing import Optional, Tuple
+
+import numpy as np
+import pandas as pd
+import zarr
+from scipy.ndimage import binary_dilation
+
+from elf.parallel.local_maxima import find_local_maxima
+from elf.parallel.distance_transform import map_points_to_objects
+from flamingo_tools.file_utils import read_image_data
+from flamingo_tools.segmentation.unet_prediction import prediction_impl
+
+
+def map_and_filter_detections(
+    segmentation: np.ndarray,
+    detections: pd.DataFrame,
+    max_distance: float,
+    resolution: float = 0.38,
+    n_threads: Optional[int] = None,
+    verbose: bool = True,
+) -> pd.DataFrame:
+    """Map synapse detections to segmented IHCs and filter out detections above a distance threshold to the IHCs.
+
+    Args:
+        segmentation: The IHC segmentation.
+        detections: The synapse marker detections.
+        max_distance: The maximal distance in micrometer for a valid match of synapse markers to IHCs.
+        resolution: The resolution / voxel size of the data in micrometer.
+        n_threads: The number of threads for parallelizing the mapping of detections to objects.
+        verbose: Whether to print the progress of the mapping procedure.
+
+    Returns:
+        The filtered dataframe with the detections mapped to the segmentation.
+    """
+    # Get the point coordinates.
+    points = detections[["z", "y", "x"]].values.astype("int")
+
+    # Set the block shape (this could also be exposed as a parameter; it should not matter much though).
+    block_shape = (64, 256, 256)
+
+    # Determine the halo. We set it to 2 pixels + the max-distance in pixels, to ensure all distances
+    # that are smaller than the max distance are measured.
+    halo = (2 + int(np.ceil(max_distance / resolution)),) * 3
+
+    # Map the detections to the obejcts in the (IHC) segmentation.
+    object_ids, object_distances = map_points_to_objects(
+        segmentation=segmentation,
+        points=points,
+        block_shape=block_shape,
+        halo=halo,
+        sampling=resolution,
+        n_threads=n_threads,
+        verbose=verbose,
+    )
+    assert len(object_ids) == len(points)
+    assert len(object_distances) == len(points)
+
+    # Add matched ids and distances to the dataframe.
+    detections["matched_ihc"] = object_ids
+    detections["distance_to_ihc"] = object_distances
+
+    # Filter the dataframe by the max distance.
+    detections = detections[detections.distance_to_ihc < max_distance]
+    return detections
+
+
+def run_prediction(
+    input_path: str,
+    input_key: str,
+    output_folder: str,
+    model_path: str,
+    block_shape: Optional[Tuple[int, int, int]] = None,
+    halo: Optional[Tuple[int, int, int]] = None,
+):
+    """Run prediction for synapse detection.
+
+    Args:
+        input_path: Input path to image channel for synapse detection.
+        input_key: Input key for resolution of image channel and mask channel.
+        output_folder: Output folder for synapse segmentation and marker detection.
+        model_path: Path to model for synapse detection.
+        block_shape: The block-shape for running the prediction.
+        halo: The halo (= block overlap) to use for prediction.
+    """
+    if block_shape is None:
+        block_shape = (64, 256, 256)
+    if halo is None:
+        halo = (16, 64, 64)
+
+    # Skip existing prediction, which is saved in output_folder/predictions.zarr
+    skip_prediction = False
+    output_path = os.path.join(output_folder, "predictions.zarr")
+    prediction_key = "prediction"
+    if os.path.exists(output_path) and prediction_key in zarr.open(output_path, "r"):
+        skip_prediction = True
+
+    if not skip_prediction:
+        prediction_impl(
+            input_path, input_key, output_folder, model_path,
+            scale=None, block_shape=block_shape, halo=halo,
+            apply_postprocessing=False, output_channels=1,
+        )
+
+    detection_path = os.path.join(output_folder, "synapse_detection.tsv")
+    if not os.path.exists(detection_path):
+        input_ = zarr.open(output_path, "r")[prediction_key]
+        detections = find_local_maxima(
+            input_, block_shape=block_shape, min_distance=2, threshold_abs=0.5, verbose=True, n_threads=16,
+        )
+        # Save the result in mobie compatible format.
+        detections = np.concatenate(
+            [np.arange(1, len(detections) + 1)[:, None], detections[:, ::-1]], axis=1
+        )
+        detections = pd.DataFrame(detections, columns=["spot_id", "x", "y", "z"])
+        detections.to_csv(detection_path, index=False, sep="\t")
+
+
+def marker_detection(
+    input_path: str,
+    input_key: str,
+    mask_path: str,
+    output_folder: str,
+    model_path: str,
+    mask_input_key: str = "s4",
+    max_distance: float = 20,
+    resolution: float = 0.38,
+):
+    """Streamlined workflow for marker detection, mapping, and filtering.
+
+    Args:
+        input_path: Input path to image channel for synapse detection.
+        input_key: Input key for resolution of image channel and mask channel.
+        mask_path: Path to IHC segmentation used to mask input.
+        output_folder: Output folder for synapse segmentation and marker detection.
+        model_path: Path to model for synapse detection.
+        mask_input_key: Key to undersampled IHC segmentation for masking input for synapse detection.
+        max_distance: The maximal distance for a valid match of synapse markers to IHCs.
+        resolution: The resolution / voxel size of the data in micrometer.
+    """
+
+    # 1.) Determine mask for inference based on the IHC segmentation.
+    # Best approach: load IHC segmentation at a low scale level, binarize it,
+    # dilate it and use this as mask. It can be mapped back to the full resolution
+    # with `elf.wrapper.ResizedVolume`.
+
+    skip_masking = False
+
+    mask_preprocess_key = "mask"
+    output_file = os.path.join(output_folder, "mask.zarr")
+
+    if os.path.exists(output_file) and mask_preprocess_key in zarr.open(output_file, "r"):
+        skip_masking = True
+
+    if not skip_masking:
+        mask_ = read_image_data(mask_path, mask_input_key)
+        new_mask = np.zeros(mask_.shape)
+        new_mask[mask_ != 0] = 1
+        arr_bin = binary_dilation(mask_, structure=np.ones((9, 9, 9))).astype(int)
+
+        with zarr.open(output_file, mode="w") as f_out:
+            f_out.create_dataset(mask_preprocess_key, data=arr_bin, compression="gzip")
+
+    # 2.) Run inference and detection of maxima.
+    # This can be taken from 'scripts/synapse_marker_detection/run_prediction.py'
+    # (And the run prediction script should then be refactored).
+
+    block_shape = (64, 256, 256)
+    halo = (16, 64, 64)
+
+    # Skip existing prediction, which is saved in output_folder/predictions.zarr
+    skip_prediction = False
+    output_path = os.path.join(output_folder, "predictions.zarr")
+    prediction_key = "prediction"
+    if os.path.exists(output_path) and prediction_key in zarr.open(output_path, "r"):
+        skip_prediction = True
+
+    if not skip_prediction:
+        prediction_impl(
+            input_path, input_key, output_folder, model_path,
+            scale=None, block_shape=block_shape, halo=halo,
+            apply_postprocessing=False, output_channels=1,
+        )
+
+    detection_path = os.path.join(output_folder, "synapse_detection.tsv")
+    if not os.path.exists(detection_path):
+        input_ = zarr.open(output_path, "r")[prediction_key]
+        detections = find_local_maxima(
+            input_, block_shape=block_shape, min_distance=2, threshold_abs=0.5, verbose=True, n_threads=16,
+        )
+        # Save the result in mobie compatible format.
+        detections = np.concatenate(
+            [np.arange(1, len(detections) + 1)[:, None], detections[:, ::-1]], axis=1
+        )
+        detections = pd.DataFrame(detections, columns=["spot_id", "x", "y", "z"])
+        detections.to_csv(detection_path, index=False, sep="\t")
+
+    else:
+        with open(detection_path, 'r') as f:
+            detections = pd.read_csv(f, sep="\t")
+
+    # 3.) Map the detections to IHC and filter them based on a distance criterion.
+    # Use the function 'map_and_filter_detections' from above.
+    input_ = read_image_data(mask_path, input_key)
+
+    detections_filtered = map_and_filter_detections(
+        segmentation=input_,
+        detections=detections,
+        max_distance=max_distance,
+        resolution=resolution,
+    )
+
+    # 4.) Add the filtered detections to MoBIE.
+    # IMPORTANT scale the coordinates with the resolution here.
+    detections_filtered["distance_to_ihc"] *= resolution
+    detections_filtered["x"] *= resolution
+    detections_filtered["y"] *= resolution
+    detections_filtered["z"] *= resolution
+    detection_path = os.path.join(output_folder, "synapse_detection_filtered.tsv")
+    detections_filtered.to_csv(detection_path, index=False, sep="\t")

flamingo_tools/segmentation/unet_prediction.py

@@ -77,15 +77,21 @@ def prediction_impl(
         else:
             model = torch.load(model_path, weights_only=False)
 
+    input_ = read_image_data(input_path, input_key)
+    chunks = getattr(input_, "chunks", (64, 64, 64))
     mask_path = os.path.join(output_folder, "mask.zarr")
+
     if os.path.exists(mask_path):
         image_mask = z5py.File(mask_path, "r")["mask"]
+        # resize mask
+        image_shape = input_.shape
+        mask_shape = image_mask.shape
+        if image_shape != mask_shape:
+            image_mask = ResizedVolume(image_mask, image_shape, order=0)
+
     else:
         image_mask = None
 
-    input_ = read_image_data(input_path, input_key)
-    chunks = getattr(input_, "chunks", (64, 64, 64))
-
     if scale is None or np.isclose(scale, 1):
         original_shape = None
     else: