Add parameter to DiceMetric and DiceHelper classes

monai/metrics/meandice.py

@@ -11,13 +11,19 @@
 
 from __future__ import annotations
 
+import numpy as np
 import torch
 
 from monai.metrics.utils import do_metric_reduction
 from monai.utils import MetricReduction, deprecated_arg
+from monai.utils.module import optional_import
 
 from .metric import CumulativeIterationMetric
 
+distance_transform_edt, has_ndimage = optional_import("scipy.ndimage", name="distance_transform_edt")
+generate_binary_structure, _ = optional_import("scipy.ndimage", name="generate_binary_structure")
+sn_label, _ = optional_import("scipy.ndimage", name="label")
+
 __all__ = ["DiceMetric", "compute_dice", "DiceHelper"]
 
 
@@ -95,6 +101,9 @@ class DiceMetric(CumulativeIterationMetric):
             If `True`, use "label_{index}" as the key corresponding to C channels; if ``include_background`` is True,
             the index begins at "0", otherwise at "1". It can also take a list of label names.
             The outcome will then be returned as a dictionary.
+        per_component: whether to compute the Dice metric per connected component. If `True`, the metric will be
+            computed for each connected component in the ground truth, and then averaged. This requires 5D binary
+            segmentations with 2 channels (background + foreground) as input. This is a more fine-grained computation.
 
     """
 
@@ -106,6 +115,7 @@ def __init__(
         ignore_empty: bool = True,
         num_classes: int | None = None,
         return_with_label: bool | list[str] = False,
+        per_component: bool = False,
     ) -> None:
         super().__init__()
         self.include_background = include_background
@@ -114,13 +124,15 @@ def __init__(
         self.ignore_empty = ignore_empty
         self.num_classes = num_classes
         self.return_with_label = return_with_label
+        self.per_component = per_component
         self.dice_helper = DiceHelper(
             include_background=self.include_background,
             reduction=MetricReduction.NONE,
             get_not_nans=False,
             apply_argmax=False,
             ignore_empty=self.ignore_empty,
             num_classes=self.num_classes,
+            per_component=self.per_component,
         )
 
     def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:  # type: ignore[override]
@@ -175,6 +187,7 @@ def compute_dice(
     include_background: bool = True,
     ignore_empty: bool = True,
     num_classes: int | None = None,
+    per_component: bool = False,
 ) -> torch.Tensor:
     """
     Computes Dice score metric for a batch of predictions. This performs the same computation as
@@ -192,6 +205,9 @@ def compute_dice(
         num_classes: number of input channels (always including the background). When this is ``None``,
             ``y_pred.shape[1]`` will be used. This option is useful when both ``y_pred`` and ``y`` are
             single-channel class indices and the number of classes is not automatically inferred from data.
+        per_component: whether to compute the Dice metric per connected component. If `True`, the metric will be
+            computed for each connected component in the ground truth, and then averaged. This requires 5D binary
+            segmentations with 2 channels (background + foreground) as input. This is a more fine-grained computation.
 
     Returns:
         Dice scores per batch and per class, (shape: [batch_size, num_classes]).
@@ -204,6 +220,7 @@ def compute_dice(
         apply_argmax=False,
         ignore_empty=ignore_empty,
         num_classes=num_classes,
+        per_component=per_component,
     )(y_pred=y_pred, y=y)
 
 
@@ -246,6 +263,9 @@ class DiceHelper:
         num_classes: number of input channels (always including the background). When this is ``None``,
             ``y_pred.shape[1]`` will be used. This option is useful when both ``y_pred`` and ``y`` are
             single-channel class indices and the number of classes is not automatically inferred from data.
+        per_component: whether to compute the Dice metric per connected component. If `True`, the metric will be
+            computed for each connected component in the ground truth, and then averaged. This requires 5D binary
+            segmentations with 2 channels (background + foreground) as input. This is a more fine-grained computation.
     """
 
     @deprecated_arg("softmax", "1.5", "1.7", "Use `apply_argmax` instead.", new_name="apply_argmax")
@@ -262,6 +282,7 @@ def __init__(
         num_classes: int | None = None,
         sigmoid: bool | None = None,
         softmax: bool | None = None,
+        per_component: bool = False,
     ) -> None:
         # handling deprecated arguments
         if sigmoid is not None:
@@ -277,6 +298,81 @@ def __init__(
         self.activate = activate
         self.ignore_empty = ignore_empty
         self.num_classes = num_classes
+        self.per_component = per_component
+
+    def compute_voronoi_regions_fast(self, labels, connectivity=26, sampling=None):
+        """
+        Voronoi assignment to connected components (CPU, single EDT) without cc3d.
+        Returns the ID of the nearest component for each voxel.
+
+        Args:
+            labels (np.ndarray | torch.Tensor): Label map where values > 0 are seeds.
+            connectivity (int): 6, 18, or 26 for 3D connectivity. Defaults to 26.
+            sampling (tuple[float, ...] | None): Voxel spacing for anisotropic distances.
+
+        Returns:
+            torch.Tensor: Voronoi region IDs (int32) on CPU.
+        """
+        if not has_ndimage:
+            raise RuntimeError("scipy.ndimage is required for per_component Dice computation.")
+        x = np.asarray(labels)
+        conn_rank = {6: 1, 18: 2, 26: 3}.get(connectivity, 3)
+        structure = generate_binary_structure(rank=3, connectivity=conn_rank)
+        cc, num = sn_label(x > 0, structure=structure)
+        if num == 0:
+            return torch.zeros_like(torch.from_numpy(x), dtype=torch.int32)
+        edt_input = np.ones(cc.shape, dtype=np.uint8)
+        edt_input[cc > 0] = 0
+        indices = distance_transform_edt(edt_input, sampling=sampling, return_distances=False, return_indices=True)
+        voronoi = cc[tuple(indices)]
+        return torch.from_numpy(voronoi)
+
+    def compute_cc_dice(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+        """
+        Compute per-component Dice for a single batch item.
+
+        Args:
+            y_pred (torch.Tensor): Predictions with shape (1, 2, D, H, W).
+            y (torch.Tensor): Ground truth with shape (1, 2, D, H, W).
+
+        Returns:
+            torch.Tensor: Mean Dice over connected components.
+        """
+        data = []
+        if y_pred.ndim == y.ndim:
+            y_pred_idx = torch.argmax(y_pred, dim=1)
+            y_idx = torch.argmax(y, dim=1)
+        else:
+            y_pred_idx = y_pred
+            y_idx = y
+        if y_idx[0].sum() == 0:
+            if self.ignore_empty:
+                data.append(torch.tensor(float("nan"), device=y_idx.device))
+            elif y_pred_idx.sum() == 0:
+                data.append(torch.tensor(1.0, device=y_idx.device))
+            else:
+                data.append(torch.tensor(0.0, device=y_idx.device))
+        else:
+            cc_assignment = self.compute_voronoi_regions_fast(y_idx[0])
+            uniq, inv = torch.unique(cc_assignment.view(-1), return_inverse=True)
+            nof_components = uniq.numel()
+            code = (y_idx.view(-1) << 1) | y_pred_idx.view(-1)
+            idx = (inv << 2) | code
+            hist = torch.bincount(idx, minlength=nof_components * 4).reshape(-1, 4)
+            _, fp, fn, tp = hist[:, 0], hist[:, 1], hist[:, 2], hist[:, 3]
+            denom = 2 * tp + fp + fn
+            dice_scores = torch.where(
+                denom > 0, (2 * tp).float() / denom.float(), torch.tensor(1.0, device=denom.device)
+            )
+            data.append(dice_scores.unsqueeze(-1))
+            data = [
+                torch.where(torch.isinf(x), torch.tensor(0.0, dtype=torch.float32, device=x.device), x) for x in data
+            ]
+            data = [
+                torch.where(torch.isnan(x), torch.tensor(0.0, dtype=torch.float32, device=x.device), x) for x in data
+            ]
+        data = [x.reshape(-1, 1) for x in data]
+        return torch.stack([x.mean() for x in data])
 
     def compute_channel(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
         """
@@ -322,15 +418,25 @@ def __call__(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor | tupl
                 y_pred = torch.sigmoid(y_pred)
             y_pred = y_pred > 0.5
 
-        first_ch = 0 if self.include_background else 1
+        if self.per_component:
+            if len(y_pred.shape) != 5 or len(y.shape) != 5 or y_pred.shape[1] != 2 or y.shape[1] != 2:
+                raise ValueError(
+                    "per_component requires both y_pred and y to be 5D binary segmentations "
+                    f"with 2 channels. Got y_pred={tuple(y_pred.shape)}, y={tuple(y.shape)}."
+                )
+
+        first_ch = 0 if self.include_background and not self.per_component else 1
         data = []
         for b in range(y_pred.shape[0]):
             c_list = []
             for c in range(first_ch, n_pred_ch) if n_pred_ch > 1 else [1]:
                 x_pred = (y_pred[b, 0] == c) if (y_pred.shape[1] == 1) else y_pred[b, c].bool()
                 x = (y[b, 0] == c) if (y.shape[1] == 1) else y[b, c]
                 c_list.append(self.compute_channel(x_pred, x))
+            if self.per_component:
+                c_list = [self.compute_cc_dice(y_pred=y_pred[b].unsqueeze(0), y=y[b].unsqueeze(0))]
             data.append(torch.stack(c_list))
+
         data = torch.stack(data, dim=0).contiguous()  # type: ignore
 
         f, not_nans = do_metric_reduction(data, self.reduction)  # type: ignore

tests/metrics/test_compute_meandice.py

@@ -18,6 +18,9 @@
 from parameterized import parameterized
 
 from monai.metrics import DiceHelper, DiceMetric, compute_dice
+from monai.utils.module import optional_import
+
+_, has_ndimage = optional_import("scipy.ndimage")
 
 _device = "cuda:0" if torch.cuda.is_available() else "cpu"
 # keep background
@@ -250,6 +253,24 @@
     {"label_1": 0.4000, "label_2": 0.6667},
 ]
 
+# Testcase for per_component DiceMetric
+y = torch.zeros((5, 2, 64, 64, 64))
+y_hat = torch.zeros((5, 2, 64, 64, 64))
+
+y[0, 1, 20:25, 20:25, 20:25] = 1
+y[0, 1, 40:45, 40:45, 40:45] = 1
+y[0, 0] = 1 - y[0, 1]
+
+y_hat[0, 1, 21:26, 21:26, 21:26] = 1
+y_hat[0, 1, 41:46, 39:44, 41:46] = 1
+y_hat[0, 0] = 1 - y_hat[0, 1]
+
+TEST_CASE_16 = [
+    {"per_component": True, "ignore_empty": False},
+    {"y": y, "y_pred": y_hat},
+    [[[0.5120]], [[1.0]], [[1.0]], [[1.0]], [[1.0]]],
+]
+
 
 class TestComputeMeanDice(unittest.TestCase):
 
@@ -301,6 +322,20 @@ def test_nans_class(self, params, input_data, expected_value):
         else:
             np.testing.assert_allclose(result.cpu().numpy(), expected_value, atol=1e-4)
 
+    # CC DiceMetric  tests
+    @parameterized.expand([TEST_CASE_16])
+    @unittest.skipUnless(has_ndimage, "Requires scipy.ndimage.")
+    def test_cc_dice_value(self, params, input_data, expected_value):
+        dice_metric = DiceMetric(**params)
+        dice_metric(**input_data)
+        result = dice_metric.aggregate(reduction="none")
+        np.testing.assert_allclose(result.cpu().numpy(), expected_value, atol=1e-4)
+
+    @unittest.skipUnless(has_ndimage, "Requires scipy.ndimage.")
+    def test_input_dimensions(self):
+        with self.assertRaises(ValueError):
+            DiceMetric(per_component=True)(torch.ones([3, 3, 144, 144]), torch.ones([3, 3, 145, 145]))
+
 
 if __name__ == "__main__":
     unittest.main()