Merge pull request #160 from anwai98/aa-ws

Add Watershed Segmentation Functionality

experiments/vision-transformer/unetr/livecell_unetr.py

@@ -7,11 +7,12 @@
 from typing import Tuple, List
 
 import imageio.v2 as imageio
-from elf.evaluation import dice_score
 from skimage.segmentation import find_boundaries
+from elf.evaluation import dice_score, mean_segmentation_accuracy
 
 import torch
 import torch_em
+from torch_em.util import segmentation
 from torch_em.transform.raw import standardize
 from torch_em.data.datasets import get_livecell_loader
 from torch_em.util.prediction import predict_with_halo
@@ -126,6 +127,16 @@ def do_unetr_inference(
         model.to(device)
         model.eval()
 
+        fg_save_dir = os.path.join(root_save_dir, f"src-{ctype}", "foreground")
+        bd_save_dir = os.path.join(root_save_dir, f"src-{ctype}", "boundary")
+        ws1_save_dir = os.path.join(root_save_dir, f"src-{ctype}", "watershed1")
+        ws2_save_dir = os.path.join(root_save_dir, f"src-{ctype}", "watershed2")
+
+        os.makedirs(fg_save_dir, exist_ok=True)
+        os.makedirs(bd_save_dir, exist_ok=True)
+        os.makedirs(ws1_save_dir, exist_ok=True)
+        os.makedirs(ws2_save_dir, exist_ok=True)
+
         with torch.no_grad():
             for img_path in tqdm(glob(test_img_dir), desc=f"Run inference for all livecell with model {model_ckpt}"):
                 fname = os.path.split(img_path)[-1]
@@ -138,14 +149,13 @@ def do_unetr_inference(
 
                 fg, bd = outputs[0, :, :], outputs[1, :, :]
 
-                fg_save_dir = os.path.join(root_save_dir, f"src-{ctype}", "foreground")
-                bd_save_dir = os.path.join(root_save_dir, f"src-{ctype}", "boundary")
-
-                os.makedirs(fg_save_dir, exist_ok=True)
-                os.makedirs(bd_save_dir, exist_ok=True)
+                ws1 = segmentation.watershed_from_components(bd, fg, min_size=10)
+                ws2 = segmentation.watershed_from_maxima(bd, fg, min_size=10, min_distance=1)
 
                 imageio.imwrite(os.path.join(fg_save_dir, fname), fg)
                 imageio.imwrite(os.path.join(bd_save_dir, fname), bd)
+                imageio.imwrite(os.path.join(ws1_save_dir, fname), ws1)
+                imageio.imwrite(os.path.join(ws2_save_dir, fname), ws2)
 
 
 def do_unetr_evaluation(
@@ -156,6 +166,7 @@ def do_unetr_evaluation(
         source_choice: str
 ):
     fg_list, bd_list = [], []
+    ws1_msa_list, ws2_msa_list, ws1_sa50_list, ws2_sa50_list = [], [], [], []
 
     for c1 in cell_types:
         _save_dir = os.path.join(root_save_dir, f"src-{c1}")
@@ -164,18 +175,24 @@ def do_unetr_evaluation(
             continue
 
         fg_set, bd_set = {"CELL TYPE": c1}, {"CELL TYPE": c1}
+        ws1_msa_set, ws2_msa_set, ws1_sa50_set, ws2_sa50_set = {"CELL TYPE": c1}, {"CELL TYPE": c1}, {"CELL TYPE": c1}, {"CELL TYPE": c1}
         for c2 in tqdm(cell_types, desc=f"Evaluation on {c1} source models from {_save_dir}"):
             fg_dir = os.path.join(_save_dir, "foreground")
             bd_dir = os.path.join(_save_dir, "boundary")
+            ws1_dir = os.path.join(_save_dir, "watershed1")
+            ws2_dir = os.path.join(_save_dir, "watershed2")
 
             gt_dir = os.path.join(input_path, "annotations", "livecell_test_images", c2, "*")
             cwise_fg, cwise_bd = [], []
+            cwise_ws1_msa, cwise_ws2_msa, cwise_ws1_sa50, cwise_ws2_sa50 = [], [], [], []
             for gt_path in glob(gt_dir):
                 fname = os.path.split(gt_path)[-1]
 
                 gt = imageio.imread(gt_path)
                 fg = imageio.imread(os.path.join(fg_dir, fname))
                 bd = imageio.imread(os.path.join(bd_dir, fname))
+                ws1 = imageio.imread(os.path.join(ws1_dir, fname))
+                ws2 = imageio.imread(os.path.join(ws2_dir, fname))
 
                 true_bd = find_boundaries(gt)
 
@@ -191,21 +208,45 @@ def do_unetr_evaluation(
                 # For the ground-truth we have already a binary label, so we don't need to threshold it again.
                 cwise_bd.append(dice_score(bd, true_bd, threshold_gt=None, threshold_seg=None))
 
+                msa1, sa_acc1 = mean_segmentation_accuracy(ws1, gt, return_accuracies=True)  # type: ignore
+                msa2, sa_acc2 = mean_segmentation_accuracy(ws2, gt, return_accuracies=True)  # type: ignore
+
+                cwise_ws1_msa.append(msa1)
+                cwise_ws2_msa.append(msa2)
+                cwise_ws1_sa50.append(sa_acc1[0])
+                cwise_ws2_sa50.append(sa_acc2[0])
+
             fg_set[c2] = np.mean(cwise_fg)
             bd_set[c2] = np.mean(cwise_bd)
+            ws1_msa_set[c2] = np.mean(cwise_ws1_msa)
+            ws2_msa_set[c2] = np.mean(cwise_ws2_msa)
+            ws1_sa50_set[c2] = np.mean(cwise_ws1_sa50)
+            ws2_sa50_set[c2] = np.mean(cwise_ws2_sa50)
 
         fg_list.append(pd.DataFrame.from_dict([fg_set]))  # type: ignore
         bd_list.append(pd.DataFrame.from_dict([bd_set]))  # type: ignore
+        ws1_msa_list.append(pd.DataFrame.from_dict([ws1_msa_set]))  # type: ignore
+        ws2_msa_list.append(pd.DataFrame.from_dict([ws2_msa_set]))  # type: ignore
+        ws1_sa50_list.append(pd.DataFrame.from_dict([ws1_sa50_set]))  # type: ignore
+        ws2_sa50_list.append(pd.DataFrame.from_dict([ws2_sa50_set]))  # type: ignore
 
     f_df_fg = pd.concat(fg_list, ignore_index=True)
     f_df_bd = pd.concat(bd_list, ignore_index=True)
+    f_df_ws1_msa = pd.concat(ws1_msa_list, ignore_index=True)
+    f_df_ws2_msa = pd.concat(ws2_msa_list, ignore_index=True)
+    f_df_ws1_sa50 = pd.concat(ws1_sa50_list, ignore_index=True)
+    f_df_ws2_sa50 = pd.concat(ws2_sa50_list, ignore_index=True)
 
-    csv_save_dir = "./results/"
+    tmp_csv_name = f"{source_choice}-sam" if sam_initialization else f"{source_choice}-scratch"
+    csv_save_dir = f"./results/{tmp_csv_name}"
     os.makedirs(csv_save_dir, exist_ok=True)
 
-    tmp_csv_name = f"{source_choice}-sam" if sam_initialization else f"{source_choice}-scratch"
-    f_df_fg.to_csv(os.path.join(csv_save_dir, f"foreground-unetr-{tmp_csv_name}-results.csv"))
-    f_df_bd.to_csv(os.path.join(csv_save_dir, f"boundary-unetr-{tmp_csv_name}-results.csv"))
+    f_df_fg.to_csv(os.path.join(csv_save_dir, "foreground-dice.csv"))
+    f_df_bd.to_csv(os.path.join(csv_save_dir, "boundary-dice.csv"))
+    f_df_ws1_msa.to_csv(os.path.join(csv_save_dir, "watershed1-msa.csv"))
+    f_df_ws2_msa.to_csv(os.path.join(csv_save_dir, "watershed2-msa.csv"))
+    f_df_ws1_sa50.to_csv(os.path.join(csv_save_dir, "watershed1-sa50.csv"))
+    f_df_ws2_sa50.to_csv(os.path.join(csv_save_dir, "watershed2-sa50.csv"))
 
 
 def main(args):

torch_em/util/segmentation.py

@@ -1,23 +1,28 @@
 import numpy as np
-import elf.segmentation as elseg
-import vigra
 
+import vigra
+import elf.segmentation as elseg
 from elf.segmentation.utils import normalize_input
+
 from skimage.measure import label
+from skimage.filters import gaussian
 from skimage.segmentation import watershed
+from skimage.feature import peak_local_max
+from scipy.ndimage import distance_transform_edt
 
 
 #
 # segmentation functionality
 #
 
+
 # could also refactor this into elf
 def size_filter(seg, min_size, hmap=None, with_background=False):
     if hmap is None:
         ids, sizes = np.unique(seg, return_counts=True)
         bg_ids = ids[sizes < min_size]
         seg[np.isin(seg, bg_ids)] = 0
-        vigra.analysis.relabelConsecutive(seg, out=seg, start_label=1, keep_zeros=True)
+        seg, _, _ = vigra.analysis.relabelConsecutive(seg.astype(np.uint), start_label=1, keep_zeros=True)
     else:
         assert hmap.ndim in (seg.ndim, seg.ndim + 1)
         hmap_ = np.max(hmap[:seg.ndim], axis=0) if hmap.ndim > seg.ndim else hmap
@@ -41,3 +46,58 @@ def connected_components_with_boundaries(foreground, boundaries, threshold=0.5):
     mask = normalize_input(foreground > threshold)
     seg = watershed(boundaries, markers=seeds, mask=mask)
     return seg.astype("uint64")
+
+
+def watershed_from_components(boundaries, foreground, min_size, threshold1=0.5, threshold2=0.5):
+    """The default approach:
+    - Subtract the boundaries from the foreground to separate touching objects.
+    - Use the connected components of this as seeds.
+    - Use the thresholded foreground predictions as mask to grow back the pieces
+      lost by subtracting the boundary prediction.
+
+    Arguments:
+        - boundaries: [np.ndarray] - The boundaries for objects
+        - foreground: [np.ndarray] - The foregrounds for objects
+        - min_size: [int] - The minimum pixels (below which) to filter objects
+        - threshold1: [float] - To separate touching objects (by subtracting bd and fg) above threshold
+        - threshold2: [float] - To threshold foreground predictions
+
+    Returns:
+        seg: [np.ndarray] - instance segmentation
+    """
+    seeds = label((foreground - boundaries) > threshold1)
+    mask = foreground > threshold2
+    seg = watershed(boundaries, seeds, mask=mask)
+    seg = size_filter(seg, min_size)
+    return seg
+
+
+def watershed_from_maxima(boundaries, foreground, min_size, min_distance, sigma=1.0, threshold1=0.5):
+    """Find objects via seeded watershed starting from the maxima of the distance transform instead.
+    This has the advantage that objects can be better separated, but it may over-segment
+    if the objects have complex shapes.
+
+    The min_distance parameter controls the minimal distance between seeds, which
+    corresponds to the minimal distance between object centers.
+
+    Arguments:
+        - boundaries: [np.ndarray] - The boundaries for objects
+        - foreground: [np.ndarray] - The foreground for objects
+        - min_size: [int] - min. pixels (below which) to filter objects
+        - min_distance: [int] - min. distance of peaks (see `from skimage.feature import peak_local_max`)
+        - sigma: [float] - standard deviation for gaussian kernel. (see `from skimage.filters import gaussian`)
+        - threshold1: [float] - To threshold foreground predictions
+
+    Returns
+        seg: [np.ndarray] - instance segmentation
+    """
+    mask = foreground > threshold1
+    boundary_distances = distance_transform_edt(boundaries < 0.1)
+    boundary_distances[~mask] = 0  # type: ignore
+    boundary_distances = gaussian(boundary_distances, sigma)  # type: ignore
+    seed_points = peak_local_max(boundary_distances, min_distance=min_distance, exclude_border=False)
+    seeds = np.zeros(mask.shape, dtype="uint32")
+    seeds[seed_points[:, 0], seed_points[:, 1]] = np.arange(1, len(seed_points) + 1)
+    seg = watershed(boundaries, markers=seeds, mask=foreground)
+    seg = size_filter(seg, min_size)
+    return seg