Merge branch 'master' into measurements

Author: constantinpape

environment.yaml

@@ -14,4 +14,5 @@ dependencies:
   - torch_em
   - trimesh
   - z5py
-  - zarr
+  # Don't install zarr v3, as we are not sure that it is compatible with MoBIE etc. yet
+  - zarr <3

flamingo_tools/file_utils.py

@@ -7,6 +7,11 @@
 import zarr
 from elf.io import open_file
 
+try:
+    from zarr.abc.store import Store
+except ImportError:
+    from zarr._storage.store import BaseStore as Store
+
 
 def _parse_shape(metadata_file):
     depth, height, width = None, None, None
@@ -62,7 +67,7 @@ def read_tif(file_path: str) -> Union[np.ndarray, np.memmap]:
     return x
 
 
-def read_image_data(input_path: Union[str, zarr.storage.FSStore], input_key: Optional[str]) -> np.typing.ArrayLike:
+def read_image_data(input_path: Union[str, Store], input_key: Optional[str]) -> np.typing.ArrayLike:
     """Read flamingo image data, stored in various formats.
 
     Args:

flamingo_tools/s3_utils.py

@@ -7,6 +7,11 @@
 import s3fs
 import zarr
 
+try:
+    from zarr.abc.store import Store
+except ImportError:
+    from zarr._storage.store import BaseStore as Store
+
 
 # Dedicated bucket for cochlea lightsheet project
 MOBIE_FOLDER = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/mobie_project/cochlea-lightsheet"
@@ -93,7 +98,7 @@ def get_s3_path(
     bucket_name: Optional[str] = None,
     service_endpoint: Optional[str] = None,
     credential_file: Optional[str] = None,
-) -> Tuple[zarr.storage.FSStore, s3fs.core.S3FileSystem]:
+) -> Tuple[Store, s3fs.core.S3FileSystem]:
     """Get S3 path for a file or folder and file system based on S3 parameters and credentials.
 
     Args:

flamingo_tools/segmentation/postprocessing.py

@@ -1,15 +1,17 @@
+import math
 import multiprocessing as mp
 from concurrent import futures
-from typing import Callable, Tuple, Optional
+from typing import Callable, List, Optional, Tuple
 
 import elf.parallel as parallel
 import numpy as np
 import nifty.tools as nt
+import networkx as nx
 import pandas as pd
 
 from elf.io import open_file
-from scipy.spatial import distance
 from scipy.sparse import csr_matrix
+from scipy.spatial import distance
 from scipy.spatial import cKDTree, ConvexHull
 from skimage import measure
 from sklearn.neighbors import NearestNeighbors
@@ -213,3 +215,261 @@ def filter_chunk(block_id):
     )
 
     return n_ids, n_ids_filtered
+
+
+def erode_subset(
+    table: pd.DataFrame,
+    iterations: int = 1,
+    min_cells: Optional[int] = None,
+    threshold: int = 35,
+    keyword: str = "distance_nn100",
+) -> pd.DataFrame:
+    """Erode coordinates of dataframe according to a keyword and a threshold.
+    Use a copy of the dataframe as an input, if it should not be edited.
+
+    Args:
+        table: Dataframe of segmentation table.
+        iterations: Number of steps for erosion process.
+        min_cells: Minimal number of rows. The erosion is stopped after falling below this limit.
+        threshold: Upper threshold for removing elements according to the given keyword.
+        keyword: Keyword of dataframe for erosion.
+
+    Returns:
+        The dataframe containing elements left after the erosion.
+    """
+    print("initial length", len(table))
+    n_neighbors = 100
+    for i in range(iterations):
+        table = table[table[keyword] < threshold]
+
+        distance_avg = nearest_neighbor_distance(table, n_neighbors=n_neighbors)
+
+        if min_cells is not None and len(distance_avg) < min_cells:
+            print(f"{i}-th iteration, length of subset {len(table)}, stopping erosion")
+            break
+
+        table.loc[:, 'distance_nn'+str(n_neighbors)] = list(distance_avg)
+
+        print(f"{i}-th iteration, length of subset {len(table)}")
+
+    return table
+
+
+def downscaled_centroids(
+    table: pd.DataFrame,
+    scale_factor: int,
+    ref_dimensions: Optional[Tuple[float, float, float]] = None,
+    downsample_mode: str = "accumulated",
+) -> np.typing.NDArray:
+    """Downscale centroids in dataframe.
+
+    Args:
+        table: Dataframe of segmentation table.
+        scale_factor: Factor for downscaling coordinates.
+        ref_dimensions: Reference dimensions for downscaling. Taken from centroids if not supplied.
+        downsample_mode: Flag for downsampling, either 'accumulated', 'capped', or 'components'.
+
+    Returns:
+        The downscaled array
+    """
+    centroids = list(zip(table["anchor_x"], table["anchor_y"], table["anchor_z"]))
+    centroids_scaled = [(c[0] / scale_factor, c[1] / scale_factor, c[2] / scale_factor) for c in centroids]
+
+    if ref_dimensions is None:
+        bounding_dimensions = (max(table["anchor_x"]), max(table["anchor_y"]), max(table["anchor_z"]))
+        bounding_dimensions_scaled = tuple([round(b // scale_factor + 1) for b in bounding_dimensions])
+        new_array = np.zeros(bounding_dimensions_scaled)
+
+    else:
+        bounding_dimensions_scaled = tuple([round(b // scale_factor + 1) for b in ref_dimensions])
+        new_array = np.zeros(bounding_dimensions_scaled)
+
+    if downsample_mode == "accumulated":
+        for c in centroids_scaled:
+            new_array[int(c[0]), int(c[1]), int(c[2])] += 1
+
+    elif downsample_mode == "capped":
+        for c in centroids_scaled:
+            new_array[int(c[0]), int(c[1]), int(c[2])] = 1
+
+    elif downsample_mode == "components":
+        if "component_labels" not in table.columns:
+            raise KeyError("Dataframe must continue key 'component_labels' for downsampling with mode 'components'.")
+        component_labels = list(table["component_labels"])
+        for comp, centr in zip(component_labels, centroids_scaled):
+            if comp != 0:
+                new_array[int(centr[0]), int(centr[1]), int(centr[2])] = comp
+
+    else:
+        raise ValueError("Choose one of the downsampling modes 'accumulated', 'capped', or 'components'.")
+
+    new_array = np.round(new_array).astype(int)
+
+    return new_array
+
+
+def components_sgn(
+    table: pd.DataFrame,
+    keyword: str = "distance_nn100",
+    threshold_erode: Optional[float] = None,
+    postprocess_graph: bool = False,
+    min_component_length: int = 50,
+    min_edge_distance: float = 30,
+    iterations_erode: Optional[int] = None,
+) -> List[List[int]]:
+    """Eroding the SGN segmentation.
+
+    Args:
+        table: Dataframe of segmentation table.
+        keyword: Keyword of the dataframe column for erosion.
+        threshold_erode: Threshold of column value after erosion step with spatial statistics.
+        postprocess_graph: Post-process graph connected components by searching for near points.
+        min_component_length: Minimal length for filtering out connected components.
+        min_edge_distance: Minimal distance in micrometer between points to create edges for connected components.
+        iterations_erode: Number of iterations for erosion, normally determined automatically.
+
+    Returns:
+        Subgraph components as lists of label_ids of dataframe.
+    """
+    centroids = list(zip(table["anchor_x"], table["anchor_y"], table["anchor_z"]))
+    labels = [int(i) for i in list(table["label_id"])]
+
+    distance_nn = list(table[keyword])
+    distance_nn.sort()
+
+    if len(table) < 20000:
+        iterations = iterations_erode if iterations_erode is not None else 0
+        min_cells = None
+        average_dist = int(distance_nn[int(len(table) * 0.8)])
+        threshold = threshold_erode if threshold_erode is not None else average_dist
+    else:
+        iterations = iterations_erode if iterations_erode is not None else 15
+        min_cells = 20000
+        threshold = threshold_erode if threshold_erode is not None else 40
+
+    print(f"Using threshold of {threshold} micrometer for eroding segmentation with keyword {keyword}.")
+
+    new_subset = erode_subset(table.copy(), iterations=iterations,
+                              threshold=threshold, min_cells=min_cells, keyword=keyword)
+
+    # create graph from coordinates of eroded subset
+    centroids_subset = list(zip(new_subset["anchor_x"], new_subset["anchor_y"], new_subset["anchor_z"]))
+    labels_subset = [int(i) for i in list(new_subset["label_id"])]
+    coords = {}
+    for index, element in zip(labels_subset, centroids_subset):
+        coords[index] = element
+
+    graph = nx.Graph()
+    for num, pos in coords.items():
+        graph.add_node(num, pos=pos)
+
+    # create edges between points whose distance is less than threshold min_edge_distance
+    for i in coords:
+        for j in coords:
+            if i < j:
+                dist = math.dist(coords[i], coords[j])
+                if dist <= min_edge_distance:
+                    graph.add_edge(i, j, weight=dist)
+
+    components = list(nx.connected_components(graph))
+
+    # remove connected components with less nodes than threshold min_component_length
+    for component in components:
+        if len(component) < min_component_length:
+            for c in component:
+                graph.remove_node(c)
+
+    components = [list(s) for s in nx.connected_components(graph)]
+
+    # add original coordinates closer to eroded component than threshold
+    if postprocess_graph:
+        threshold = 15
+        for label_id, centr in zip(labels, centroids):
+            if label_id not in labels_subset:
+                add_coord = []
+                for comp_index, component in enumerate(components):
+                    for comp_label in component:
+                        dist = math.dist(centr, centroids[comp_label - 1])
+                        if dist <= threshold:
+                            add_coord.append([comp_index, label_id])
+                            break
+                if len(add_coord) != 0:
+                    components[add_coord[0][0]].append(add_coord[0][1])
+
+    return components
+
+
+def label_components(
+    table: pd.DataFrame,
+    min_size: int = 1000,
+    threshold_erode: Optional[float] = None,
+    min_component_length: int = 50,
+    min_edge_distance: float = 30,
+    iterations_erode: Optional[int] = None,
+) -> List[int]:
+    """Label components using graph connected components.
+
+    Args:
+        table: Dataframe of segmentation table.
+        min_size: Minimal number of pixels for filtering small instances.
+        threshold_erode: Threshold of column value after erosion step with spatial statistics.
+        min_component_length: Minimal length for filtering out connected components.
+        min_edge_distance: Minimal distance in micrometer between points to create edges for connected components.
+        iterations_erode: Number of iterations for erosion, normally determined automatically.
+
+    Returns:
+        List of component label for each point in dataframe. 0 - background, then in descending order of size
+    """
+
+    # First, apply the size filter.
+    entries_filtered = table[table.n_pixels < min_size]
+    table = table[table.n_pixels >= min_size]
+
+    components = components_sgn(table, threshold_erode=threshold_erode, min_component_length=min_component_length,
+                                min_edge_distance=min_edge_distance, iterations_erode=iterations_erode)
+
+    # add size-filtered objects to have same initial length
+    table = pd.concat([table, entries_filtered], ignore_index=True)
+    table.sort_values("label_id")
+
+    length_components = [len(c) for c in components]
+    length_components, components = zip(*sorted(zip(length_components, components), reverse=True))
+
+    component_labels = [0 for _ in range(len(table))]
+    # be aware of 'label_id' of dataframe starting at 1
+    for lab, comp in enumerate(components):
+        for comp_index in comp:
+            component_labels[comp_index - 1] = lab + 1
+
+    return component_labels
+
+
+def postprocess_sgn_seg(
+    table: pd.DataFrame,
+    min_size: int = 1000,
+    threshold_erode: Optional[float] = None,
+    min_component_length: int = 50,
+    min_edge_distance: float = 30,
+    iterations_erode: Optional[int] = None,
+) -> pd.DataFrame:
+    """Postprocessing SGN segmentation of cochlea.
+
+    Args:
+        table: Dataframe of segmentation table.
+        min_size: Minimal number of pixels for filtering small instances.
+        threshold_erode: Threshold of column value after erosion step with spatial statistics.
+        min_component_length: Minimal length for filtering out connected components.
+        min_edge_distance: Minimal distance in micrometer between points to create edges for connected components.
+        iterations_erode: Number of iterations for erosion, normally determined automatically.
+
+    Returns:
+        Dataframe with component labels.
+    """
+
+    comp_labels = label_components(table, min_size=min_size, threshold_erode=threshold_erode,
+                                   min_component_length=min_component_length,
+                                   min_edge_distance=min_edge_distance, iterations_erode=iterations_erode)
+
+    table.loc[:, "component_labels"] = comp_labels
+
+    return table

scripts/prediction/expand_seg_table.py

@@ -61,7 +61,7 @@ def main(
             neighbor_counts = [n[0] for n in neighbor_counts]
             tsv_table['neighbors_in_radius'+str(r_neighbor)] = neighbor_counts
 
-    tsv_table.to_csv(out_path, sep="\t")
+    tsv_table.to_csv(out_path, sep="\t", index=False)
 
 
 if __name__ == "__main__":