Merge pull request #46 from computational-cell-analytics/grid-search

Implement grid-search for IHC segmentation

Author: constantinpape

flamingo_tools/segmentation/unet_prediction.py

@@ -8,6 +8,7 @@
 import os
 import warnings
 from concurrent import futures
+from functools import partial
 from typing import Optional, Tuple
 
 import elf.parallel as parallel
@@ -17,10 +18,11 @@
 import torch
 import z5py
 
-from elf.wrapper import ThresholdWrapper, SimpleTransformationWrapper
+from elf.wrapper import ThresholdWrapper, SimpleTransformationWrapper, SimpleTransformationWrapperWithHalo
 from elf.wrapper.base import MultiTransformationWrapper
 from elf.wrapper.resized_volume import ResizedVolume
 from elf.io import open_file
+from skimage.filters import gaussian
 from torch_em.util import load_model
 from torch_em.util.prediction import predict_with_halo
 from tqdm import tqdm
@@ -278,6 +280,7 @@ def distance_watershed_implementation(
     center_distance_threshold: float = 0.4,
     boundary_distance_threshold: Optional[float] = None,
     fg_threshold: float = 0.5,
+    distance_smoothing: float = 0.0,
     original_shape: Optional[Tuple[int, int, int]] = None
 ) -> None:
     """Parallel implementation of the distance-prediction based watershed.
@@ -290,6 +293,8 @@ def distance_watershed_implementation(
         boundary_distance_threshold: The threshold applied to the boundary predictions to derive seeds.
             By default this is set to 'None', in which case the boundary distances are not used for the seeds.
         fg_threshold: The threshold applied to the foreground prediction for deriving the watershed mask.
+        distance_smoothing: The sigma value for smoothing the distance predictions with a gaussian kernel.
+            This may help to reduce border artifacts. If set to 0 (the default) smoothing is not applied.
         original_shape: The original shape to resize the segmentation to.
     """
     if isinstance(input_path, str):
@@ -307,11 +312,14 @@ def distance_watershed_implementation(
     center_distances = SelectChannel(input_, 1)
     boundary_distances = SelectChannel(input_, 2)
 
-    # Apply (lazy) smoothing to both.
-    # NOTE: this leads to issues with the parallelization, so we don't implement distance smoothing for now.
-    # smoothing = partial(ff.gaussianSmoothing, sigma=distance_smoothing)
-    # center_distances = SimpleTransformationWrapper(center_distances, transformation=smoothing)
-    # boundary_distances = SimpleTransformationWrapper(boundary_distances, transformation=smoothing)
+    # Apply (lazy) smoothing to both channels if distance smoothing was set.
+    if distance_smoothing > 0:
+        smooth = partial(gaussian, sigma=distance_smoothing)
+        # We assume that the gaussian is truncated at 5.3 sigma (tolerance of 1e-6)
+        halo = int(np.ceil(5.3 * distance_smoothing))
+        halo = 3 * (halo,)
+        center_distances = SimpleTransformationWrapperWithHalo(center_distances, transformation=smooth, halo=halo)
+        boundary_distances = SimpleTransformationWrapperWithHalo(boundary_distances, transformation=smooth, halo=halo)
 
     # Allocate the (zarr) array for the seeds.
     if output_folder is None:
@@ -427,6 +435,7 @@ def run_unet_prediction(
     center_distance_threshold: float = 0.4,
     boundary_distance_threshold: Optional[float] = None,
     fg_threshold: float = 0.5,
+    distance_smoothing: float = 0.0,
     seg_class: Optional[str] = None,
 ) -> None:
     """Run prediction and segmentation with a distance U-Net.
@@ -446,6 +455,8 @@ def run_unet_prediction(
         boundary_distance_threshold: The threshold applied to the boundary predictions to derive seeds.
             By default this is set to 'None', in which case the boundary distances are not used for the seeds.
         fg_threshold: The threshold applied to the foreground prediction for deriving the watershed mask.
+        distance_smoothing: The sigma value for smoothing the distance predictions with a gaussian kernel.
+            This may help to reduce border artifacts. If set to 0 (the default) smoothing is not applied.
         seg_class: Specifier for exclusion criterias for mask generation.
     """
     if output_folder is not None:
@@ -470,7 +481,8 @@ def run_unet_prediction(
         pmap_out, output_folder, min_size=min_size, original_shape=original_shape,
         center_distance_threshold=center_distance_threshold,
         boundary_distance_threshold=boundary_distance_threshold,
-        fg_threshold=fg_threshold
+        fg_threshold=fg_threshold,
+        distance_smoothing=distance_smoothing,
     )
 
     return segmentation
@@ -590,6 +602,7 @@ def run_unet_segmentation_slurm(
     center_distance_threshold: float = 0.4,
     boundary_distance_threshold: float = 0.5,
     fg_threshold: float = 0.5,
+    distance_smoothing: float = 0.0,
 ) -> None:
     """Create segmentation from prediction.
 
@@ -600,10 +613,13 @@ def run_unet_segmentation_slurm(
         boundary_distance_threshold: The threshold applied to the boundary predictions to derive seeds.
             By default this is set to 'None', in which case the boundary distances are not used for the seeds.
         fg_threshold: The threshold applied to the foreground prediction for deriving the watershed mask.
+        distance_smoothing: The sigma value for smoothing the distance predictions with a gaussian kernel.
+            This may help to reduce border artifacts. If set to 0 (the default) smoothing is not applied.
     """
     min_size = int(min_size)
     pmap_out = os.path.join(output_folder, "predictions.zarr")
     distance_watershed_implementation(pmap_out, output_folder, center_distance_threshold=center_distance_threshold,
                                       boundary_distance_threshold=boundary_distance_threshold,
                                       fg_threshold=fg_threshold,
+                                      distance_smoothing=distance_smoothing,
                                       min_size=min_size)

scripts/validation/IHCs/grid_search.py

@@ -0,0 +1,108 @@
+import os
+from glob import glob
+
+import imageio.v3 as imageio
+import numpy as np
+from skimage.measure import label
+from skimage.segmentation import relabel_sequential
+
+from torch_em.util import load_model
+from torch_em.util.grid_search import DistanceBasedInstanceSegmentation, instance_segmentation_grid_search
+
+ROOT = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/training_data/IHC/2025-07-for-grid-search"
+MODEL_PATH = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/trained_models/IHC/v4_cochlea_distance_unet_IHC_supervised_2025-07-14"  # noqa
+
+GRID_SEARCH_VALUES = {
+    "center_distance_threshold": [0.3, 0.4, 0.5, 0.6, 0.7],
+    "boundary_distance_threshold": [0.3, 0.4, 0.5, 0.6, 0.7],
+    "distance_smoothing": [0.0, 0.6, 1.0, 1.6],
+    "min_size": [0],
+}
+
+
+def preprocess_gt():
+    label_paths = sorted(glob(os.path.join(ROOT, "**/*_corrected.tif"), recursive=True))
+    for label_path in label_paths:
+        seg = imageio.imread(label_path)
+        seg = label(seg)
+
+        min_size = 750
+        ids, sizes = np.unique(seg, return_counts=True)
+        filter_ids = ids[sizes < min_size]
+        seg[np.isin(seg, filter_ids)] = 0
+        seg, _, _ = relabel_sequential(seg)
+
+        out_path = label_path.replace("_corrected", "_gt")
+        imageio.imwrite(out_path, seg)
+
+
+def run_grid_search():
+    image_paths = sorted(glob(os.path.join(ROOT, "**/*lut3*.tif"), recursive=True))
+    label_paths = sorted(glob(os.path.join(ROOT, "**/*_gt.tif"), recursive=True))
+    assert len(image_paths) == len(label_paths), f"{len(image_paths)}, {len(label_paths)}"
+    result_dir = "ihc-v4-gs"
+
+    block_shape = (96, 256, 256)
+    halo = (8, 64, 64)
+    model = load_model(MODEL_PATH)
+    segmenter = DistanceBasedInstanceSegmentation(model, block_shape=block_shape, halo=halo)
+
+    best_kwargs, best_score = instance_segmentation_grid_search(
+        segmenter, image_paths, label_paths, result_dir, grid_search_values=GRID_SEARCH_VALUES
+    )
+    print("Grid-search result:")
+    print(best_kwargs)
+    print(best_score)
+
+
+# TODO plot the grid search results
+def evaluate_grid_search():
+    import matplotlib.pyplot as plt
+    import pandas as pd
+    import seaborn as sns
+
+    result_dir = "ihc-v4-gs"
+    criterion = "SA50"
+
+    gs_files = glob(os.path.join(result_dir, "*.csv"))
+    gs_result = pd.concat([pd.read_csv(gs_file) for gs_file in gs_files])
+
+    grid_search_parameters = list(GRID_SEARCH_VALUES.keys())
+
+    # Retrieve only the relevant columns and group by the gridsearch columns
+    # and compute the mean value
+    gs_result = gs_result[grid_search_parameters + [criterion]].reset_index()
+    gs_result = gs_result.groupby(grid_search_parameters).mean().reset_index()
+
+    # Find the best score and best result
+    best_score, best_idx = gs_result[criterion].max(), gs_result[criterion].idxmax()
+    best_params = gs_result.iloc[best_idx]
+    best_kwargs = {k: v for k, v in zip(grid_search_parameters, best_params)}
+
+    print("Best parameters:")
+    print(best_kwargs)
+    print("With score:", best_score)
+
+    fig, axes = plt.subplots(3)
+    for i, (idx, col) in enumerate([
+        ("center_distance_threshold", "boundary_distance_threshold"),
+        ("distance_smoothing", "boundary_distance_threshold"),
+        ("distance_smoothing", "center_distance_threshold"),
+    ]):
+        res = gs_result.groupby([idx, col]).mean().reset_index()
+        res = res[[idx, col, criterion]]
+        res = res.pivot(index=idx, columns=col, values=criterion)
+        sns.heatmap(res, cmap="viridis", annot=True, fmt=".2g", cbar_kws={"label": criterion}, ax=axes[i])
+        axes[i].set_xlabel(idx)
+        axes[i].set_xlabel(col)
+    plt.show()
+
+
+def main():
+    # preprocess_gt()
+    # run_grid_search()
+    evaluate_grid_search()
+
+
+if __name__ == "__main__":
+    main()

scripts/validation/IHCs/run_evaluation.py

@@ -59,7 +59,7 @@ def main():
     parser.add_argument("-i", "--input", default=ROOT)
     parser.add_argument("--folders", default=ANNOTATION_FOLDERS)
     parser.add_argument("--result_file", default="results.csv")
-    parser.add_argument("--segmentation_name", default="IHC_v2")
+    parser.add_argument("--segmentation_name", default="IHC_v4")
     parser.add_argument("--cache_folder")
     args = parser.parse_args()
     run_evaluation(args.input, args.folders, args.result_file, args.cache_folder, args.segmentation_name)

test/test_segmentation/test_unet_prediction.py

@@ -67,7 +67,8 @@ def test_run_unet_prediction_tif_mask(self):
 
     def test_run_unet_prediction_complex_watershed(self):
         self._test_run_unet_prediction(
-            use_tif=False, use_mask=True, center_distance_threshold=0.5, boundary_distance_threshold=0.5,
+            use_tif=False, use_mask=True,
+            center_distance_threshold=0.5, boundary_distance_threshold=0.5, distance_smoothing=1.0,
         )