Update example analysis pipeline and sample data

Author: constantinpape

examples/analysis_pipeline.py

@@ -1,7 +1,88 @@
-# TODO implement analysis pipeline for our tomo sample data:
-# - segment vesicles, AZ, and compartment
-# - use compartment segmentation to find the presynaptic terminal
-# - postprocess the AZ segmentation
-# - measure distances between vesicles and AZ
-# - vesicle pool assignment into docked and non-attached terminals
-# - export table with distance and morphology measurements for the two pools
+import napari
+import pandas as pd
+
+from synapse_net.file_utils import read_mrc
+from synapse_net.sample_data import get_sample_data
+from synapse_net.tools.util import run_segmentation, get_model, compute_scale_from_voxel_size
+
+
+def segment_structures(tomogram, voxel_size):
+    # Segment the synaptic vesicles. The data will automatically be resized
+    # to match the average voxel size of the training data.
+    model_name = "vesicles_3d"  # This is the name for the vesicle model for EM tomography.
+    model = get_model(model_name)  # Load the corresponding model.
+    # Compute the scale to match the tomogram voxel size to the training data.
+    scale = compute_scale_from_voxel_size(voxel_size, model_name)
+    vesicles = run_segmentation(tomogram, model, model_name, scale=scale)
+
+    # Segment the active zone.
+    model_name = "active_zone"
+    model = get_model(model_name)
+    scale = compute_scale_from_voxel_size(voxel_size, model_name)
+    active_zone = run_segmentation(tomogram, model, model_name, scale=scale)
+
+    # Segment the synaptic compartments.
+    model_name = "compartments"
+    model = get_model(model_name)
+    scale = compute_scale_from_voxel_size(voxel_size, model_name)
+    compartments = run_segmentation(tomogram, model, model_name, scale=scale)
+
+    return {"vesicles": vesicles, "active_zone": active_zone, "compartments": compartments}
+
+
+def postprocess_segmentation(segmentations):
+    pass
+
+
+def measure_distances(segmentations):
+    pass
+
+
+def assign_vesicle_pools(distances):
+    pass
+
+
+def visualize_results(tomogram, segmentations, vesicle_pools):
+    # TODO vesicle pool visualization
+    viewer = napari.Viewer()
+    viewer.add_image(tomogram)
+    for name, segmentation in segmentations.items():
+        viewer.add_labels(segmentation, name=name)
+    napari.run()
+
+
+def save_analysis(segmentations, distances, vesicle_pools, save_path):
+    pass
+
+
+def main():
+    """This script implements an example analysis pipeline with SynapseNet and applies it to a tomogram.
+    Here, we analyze docked and non-attached vesicles in a sample tomogram."""
+
+    # Load the tomogram for our sample data.
+    mrc_path = get_sample_data("tem_tomo")
+    tomogram, voxel_size = read_mrc(mrc_path)
+
+    # Segment synaptic vesicles, the active zone, and the synaptic compartment.
+    segmentations = segment_structures(tomogram, voxel_size)
+
+    # Post-process the segmentations, to find the presynaptic terminal,
+    # filter out vesicles not in the terminal, and to 'snape' the AZ to the presynaptic boundary.
+    segmentations = postprocess_segmentation(segmentations)
+
+    # Measure the distances between the AZ and vesicles.
+    distances = measure_distances(segmentations)
+
+    # Assign the vesicle pools, 'docked' and 'non-attached' vesicles, based on the distances.
+    vesicle_pools = assign_vesicle_pools(distances)
+
+    # Visualize the results.
+    visualize_results(tomogram, segmentations, vesicle_pools)
+
+    # Compute the vesicle radii and combine and save all measurements.
+    save_path = "analysis_results.xlsx"
+    save_analysis(segmentations, distances, vesicle_pools, save_path)
+
+
+if __name__ == "__main__":
+    main()

synapse_net/sample_data.py

@@ -15,11 +15,11 @@ def get_sample_data(name: str) -> str:
     """
     registry = {
         "tem_2d.mrc": "3c6f9ff6d7673d9bf2fd46c09750c3c7dbb8fa1aa59dcdb3363b65cc774dcf28",
-        "tem_tomo.mrc": "24af31a10761b59fa6ad9f0e763f8f084304e4f31c59b482dd09dde8cd443ed7",
+        "tem_tomo.mrc": "eb790f83efb4c967c96961239ae52578d95da902fc32307629f76a26c3dc61fa",
     }
     urls = {
         "tem_2d.mrc": "https://owncloud.gwdg.de/index.php/s/5sAQ0U4puAspcHg/download",
-        "tem_tomo.mrc": "https://owncloud.gwdg.de/index.php/s/NeP7gOv76Vj26lm/download",
+        "tem_tomo.mrc": "https://owncloud.gwdg.de/index.php/s/TmLjDCXi42E49Ef/download",
     }
     key = f"{name}.mrc"