Fix incorrect truncated parameter in make_gaussian_kernel causing corrupted LocalNormalizedCrossCorrelationLoss

monai/losses/image_dissimilarity.py

@@ -35,7 +35,7 @@ def make_triangular_kernel(kernel_size: int) -> torch.Tensor:
 
 def make_gaussian_kernel(kernel_size: int) -> torch.Tensor:
     sigma = torch.tensor(kernel_size / 3.0)
-    kernel = gaussian_1d(sigma=sigma, truncated=kernel_size // 2, approx="sampled", normalize=False) * (
+    kernel = gaussian_1d(sigma=sigma, truncated=(kernel_size // 2) / sigma, approx="sampled", normalize=False) * (
         2.5066282 * sigma
     )
     return kernel[:kernel_size]

tests/integration/test_reg_loss_integration.py

@@ -26,6 +26,7 @@
     [LocalNormalizedCrossCorrelationLoss, {"kernel_size": 7, "kernel_type": "rectangular"}, ["pred", "target"]],
     [LocalNormalizedCrossCorrelationLoss, {"kernel_size": 5, "kernel_type": "triangular"}, ["pred", "target"]],
     [LocalNormalizedCrossCorrelationLoss, {"kernel_size": 3, "kernel_type": "gaussian"}, ["pred", "target"]],
+    [LocalNormalizedCrossCorrelationLoss, {"kernel_size": 7, "kernel_type": "gaussian"}, ["pred", "target"]],
     [GlobalMutualInformationLoss, {"num_bins": 10}, ["pred", "target"]],
     [GlobalMutualInformationLoss, {"kernel_type": "b-spline", "num_bins": 10}, ["pred", "target"]],
 ]
@@ -98,6 +99,23 @@ def forward(self, x):
             optimizer.step()
         self.assertGreater(init_loss, loss_val, "loss did not decrease")
 
+def test_lncc_gaussian_kernel_gt3_identical_images(self):
+        """
+        Regression test for make_gaussian_kernel truncated parameter bug.
+        LNCC on identical inputs must be close to -1.0 for gaussian kernel_size > 3.
+        """
+        for kernel_size in [5, 7]:
+            with self.subTest(kernel_size=kernel_size):
+                loss_fn = LocalNormalizedCrossCorrelationLoss(
+                    spatial_dims=2, kernel_size=kernel_size, kernel_type="gaussian"
+                ).to(self.device)
+                x = torch.rand(2, 1, 32, 32, device=self.device)
+                y = x.clone()
+                loss = loss_fn(x, y)
+                self.assertTrue(
+                    torch.allclose(loss, torch.tensor(-1.0, device=self.device, dtype=loss.dtype), atol=1e-3),
+                    f"LNCC of identical images should be -1.0, got {loss.item():.6f} (kernel_size={kernel_size})",
+                )
 
 if __name__ == "__main__":
     unittest.main()