Merge pull request #32 from computational-cell-analytics/more-complex-watershed

Implement more complex watershed logic
The newly implemented watershed logic works well for the IHC segmentation (with a value of 0.5 for the boundary distance threshold).

Author: schilling40

flamingo_tools/segmentation/unet_prediction.py

@@ -18,6 +18,7 @@
 import z5py
 
 from elf.wrapper import ThresholdWrapper, SimpleTransformationWrapper
+from elf.wrapper.base import MultiTransformationWrapper
 from elf.wrapper.resized_volume import ResizedVolume
 from elf.io import open_file
 from torch_em.util import load_model
@@ -217,61 +218,91 @@ def find_mask_block(block_id):
         list(tqdm(tp.map(find_mask_block, range(n_blocks)), total=n_blocks))
 
 
-def segmentation_impl(input_path, output_folder, min_size, original_shape=None):
-    """@private
+def distance_watershed_implementation(
+    input_path: str,
+    output_folder: str,
+    min_size: int,
+    center_distance_threshold: float = 0.4,
+    boundary_distance_threshold: Optional[float] = None,
+    fg_threshold: float = 0.5,
+    original_shape: Optional[Tuple[int, int, int]] = None,
+) -> None:
+    """Parallel implementation of the distance-prediction based watershed.
+
+    Args:
+        input_path: The path to the zarr file with the network predictions.
+        output_folder: The folder for storing the segmentation and intermediate results.
+        min_size: The minimal size of objects in the segmentation.
+        center_distance_threshold: The threshold applied to the distance center predictions to derive seeds.
+        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.
+        original_shape: The original shape to resize the segmentation to.
     """
     input_ = open_file(input_path, "r")["prediction"]
 
     # Limit the number of cores for parallelization.
     n_threads = min(16, mp.cpu_count())
 
-    # The center distances as input for computing the seeds.
+    # Get the foreground mask.
+    mask = ThresholdWrapper(SelectChannel(input_, 0), threshold=fg_threshold)
+
+    # Get the the center and boundary distances.
     center_distances = SelectChannel(input_, 1)
-    block_shape = center_distances.chunks
+    boundary_distances = SelectChannel(input_, 2)
 
-    # Compute the seeds based on smoothed center distances < 0.5.
+    # 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)
+
+    # Allocate an zarr array for the seeds.
+    block_shape = center_distances.chunks
     seed_path = os.path.join(output_folder, "seeds.zarr")
     seed_file = open_file(os.path.join(seed_path), "a")
     seeds = seed_file.require_dataset(
         "seeds", shape=center_distances.shape, chunks=block_shape, compression="gzip", dtype="uint64"
     )
 
-    fg_threshold = 0.5
-    mask = ThresholdWrapper(SelectChannel(input_, 0), threshold=fg_threshold)
+    # Compute the seed inputs:
+    # First, threshold the center distances.
+    seed_inputs = ThresholdWrapper(center_distances, threshold=center_distance_threshold, operator=np.less)
+    # Then, if a boundary distance threshold was passed threshold the boundary distances and combine both.
+    if boundary_distance_threshold is not None:
+        seed_inputs2 = ThresholdWrapper(boundary_distances, threshold=boundary_distance_threshold, operator=np.less)
+        seed_inputs = MultiTransformationWrapper(np.logical_and, seed_inputs, seed_inputs2)
 
+    # Compute the seeds via connected components on the seed inputs.
     parallel.label(
-        data=ThresholdWrapper(center_distances, threshold=0.4, operator=np.less),
-        out=seeds, block_shape=block_shape, mask=mask, verbose=True, n_threads=n_threads
+        data=seed_inputs, out=seeds, block_shape=block_shape, mask=mask, verbose=True, n_threads=n_threads
     )
 
-    # Run the watershed.
-    if original_shape is None:
-        seg_path = os.path.join(output_folder, "segmentation.zarr")
-    else:
-        seg_path = os.path.join(output_folder, "seg_downscaled.zarr")
-
+    # Allocate the zarr array for the segmentation.
+    seg_path = os.path.join(output_folder, "segmentation.zarr" if original_shape is None else "seg_downscaled.zarr")
     seg_file = open_file(seg_path, "a")
     seg = seg_file.create_dataset(
         "segmentation", shape=seeds.shape, chunks=block_shape, compression="gzip", dtype="uint64"
     )
 
-    hmap = SelectChannel(input_, 2)
+    # Compute the segmentation with a seeded watershed
     halo = (2, 8, 8)
     parallel.seeded_watershed(
-        hmap, seeds, out=seg, block_shape=block_shape, halo=halo, mask=mask, verbose=True,
+        boundary_distances, seeds, out=seg, block_shape=block_shape, halo=halo, mask=mask, verbose=True,
         n_threads=n_threads,
     )
 
+    # Apply size filter.
     if min_size > 0:
         parallel.size_filter(
             seg, seg, min_size=min_size, block_shape=block_shape, mask=mask,
             verbose=True, n_threads=n_threads, relabel=True,
         )
 
+    # Reshape to original shape if given.
     if original_shape is not None:
         out_path = os.path.join(output_folder, "segmentation.zarr")
 
-        # This logic should be refactored.
         output_seg = ResizedVolume(seg, shape=original_shape, order=0)
         with open_file(out_path, "a") as f:
             out_seg_volume = f.create_dataset(
@@ -325,6 +356,9 @@ def run_unet_prediction(
     block_shape: Optional[Tuple[int, int, int]] = None,
     halo: Optional[Tuple[int, int, int]] = None,
     use_mask: bool = True,
+    center_distance_threshold: float = 0.4,
+    boundary_distance_threshold: Optional[float] = None,
+    fg_threshold: float = 0.5,
 ) -> None:
     """Run prediction and segmentation with a distance U-Net.
 
@@ -339,6 +373,10 @@ def run_unet_prediction(
         block_shape: The block-shape for running the prediction.
         halo: The halo (= block overlap) to use for prediction.
         use_mask: Whether to use the masking heuristics to not run inference on empty blocks.
+        center_distance_threshold: The threshold applied to the distance center predictions to derive seeds.
+        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.
     """
     os.makedirs(output_folder, exist_ok=True)
 
@@ -350,7 +388,12 @@ def run_unet_prediction(
     )
 
     pmap_out = os.path.join(output_folder, "predictions.zarr")
-    segmentation_impl(pmap_out, output_folder, min_size=min_size, original_shape=original_shape)
+    distance_watershed_implementation(
+        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,
+    )
 
 
 #
@@ -467,4 +510,4 @@ def run_unet_segmentation_slurm(output_folder: str, min_size: int) -> None:
     """
     min_size = int(min_size)
     pmap_out = os.path.join(output_folder, "predictions.zarr")
-    segmentation_impl(pmap_out, output_folder, min_size=min_size)
+    distance_watershed_implementation(pmap_out, output_folder, min_size=min_size)

test/test_segmentation/test_unet_prediction.py

@@ -31,7 +31,7 @@ def _create_data(self, tmp_dir, use_tif):
                 f.create_dataset(key, data=data, chunks=(32, 32, 32))
         return path, key
 
-    def _test_run_unet_prediction(self, use_tif, use_mask):
+    def _test_run_unet_prediction(self, use_tif, use_mask, **extra_kwargs):
         from flamingo_tools.segmentation import run_unet_prediction
 
         with tempfile.TemporaryDirectory() as tmp_dir:
@@ -42,6 +42,7 @@ def _test_run_unet_prediction(self, use_tif, use_mask):
                 input_path, input_key, output_folder, model_path,
                 scale=None, min_size=100,
                 block_shape=(64, 64, 64), halo=(16, 16, 16),
+                **extra_kwargs
             )
 
             expected_path = os.path.join(output_folder, "segmentation.zarr")
@@ -64,6 +65,11 @@ def test_run_unet_prediction_tif(self):
     def test_run_unet_prediction_tif_mask(self):
         self._test_run_unet_prediction(use_tif=True, use_mask=True)
 
+    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,
+        )
+
 
 if __name__ == "__main__":
     unittest.main()