diff --git a/monai/apps/pathology/transforms/spatial/array.py b/monai/apps/pathology/transforms/spatial/array.py
index abfdf3cac0..58fef8969f 100644
--- a/monai/apps/pathology/transforms/spatial/array.py
+++ b/monai/apps/pathology/transforms/spatial/array.py
@@ -75,12 +75,13 @@ def __call__(self, image: NdarrayOrTensor) -> NdarrayOrTensor:
                 .contiguous()
             )
         elif isinstance(image, np.ndarray):
-            h_step, w_step = steps
-            c_stride, h_stride, w_stride = image.strides
+            x_step, y_step = steps
+            c_stride, x_stride, y_stride = image.strides
+            n_channels = image.shape[0]
             patches = as_strided(
                 image,
-                shape=(*self.grid_size, 3, patch_size[0], patch_size[1]),
-                strides=(h_stride * h_step, w_stride * w_step, c_stride, h_stride, w_stride),
+                shape=(*self.grid_size, n_channels, patch_size[0], patch_size[1]),
+                strides=(x_stride * x_step, y_stride * y_step, c_stride, x_stride, y_stride),
                 writeable=False,
             )
             # flatten the first two dimensions
@@ -210,17 +211,17 @@ def __call__(self, image: NdarrayOrTensor) -> NdarrayOrTensor:
             )
 
         # extact tiles
-        h_step, w_step = self.step, self.step
-        h_size, w_size = self.tile_size, self.tile_size
-        c_len, h_len, w_len = img_np.shape
-        c_stride, h_stride, w_stride = img_np.strides
+        x_step, y_step = self.step, self.step
+        h_tile, w_tile = self.tile_size, self.tile_size
+        c_image, h_image, w_image = img_np.shape
+        c_stride, x_stride, y_stride = img_np.strides
         llw = as_strided(
             img_np,
-            shape=((h_len - h_size) // h_step + 1, (w_len - w_size) // w_step + 1, c_len, h_size, w_size),
-            strides=(h_stride * h_step, w_stride * w_step, c_stride, h_stride, w_stride),
+            shape=((h_image - h_tile) // x_step + 1, (w_image - w_tile) // y_step + 1, c_image, h_tile, w_tile),
+            strides=(x_stride * x_step, y_stride * y_step, c_stride, x_stride, y_stride),
             writeable=False,
         )
-        img_np = llw.reshape(-1, c_len, h_size, w_size)
+        img_np = llw.reshape(-1, c_image, h_tile, w_tile)
 
         # if keeping all patches
         if self.tile_count is None:
diff --git a/tests/test_split_on_grid.py b/tests/test_split_on_grid.py
index a3bf2674f4..8017fcccbd 100644
--- a/tests/test_split_on_grid.py
+++ b/tests/test_split_on_grid.py
@@ -34,6 +34,11 @@
 TEST_CASE_5 = [{"grid_size": 1, "patch_size": 4}, A, torch.stack([A])]
 TEST_CASE_6 = [{"grid_size": 2, "patch_size": 2}, A, torch.stack([A11, A12, A21, A22])]
 TEST_CASE_7 = [{"grid_size": 1}, A, torch.stack([A])]
+TEST_CASE_8 = [
+    {"grid_size": (2, 2), "patch_size": 2},
+    torch.arange(12).reshape(1, 3, 4).to(torch.float32),
+    torch.Tensor([[[[0, 1], [4, 5]]], [[[2, 3], [6, 7]]], [[[4, 5], [8, 9]]], [[[6, 7], [10, 11]]]]).to(torch.float32),
+]
 
 TEST_SINGLE = []
 for p in TEST_NDARRAYS:
@@ -45,6 +50,7 @@
     TEST_SINGLE.append([p, *TEST_CASE_5])
     TEST_SINGLE.append([p, *TEST_CASE_6])
     TEST_SINGLE.append([p, *TEST_CASE_7])
+    TEST_SINGLE.append([p, *TEST_CASE_8])
 
 TEST_CASE_MC_0 = [{"grid_size": (2, 2)}, [A, A], [torch.stack([A11, A12, A21, A22]), torch.stack([A11, A12, A21, A22])]]
 TEST_CASE_MC_1 = [{"grid_size": (2, 1)}, [A] * 5, [torch.stack([A1, A2])] * 5]
diff --git a/tests/test_split_on_grid_dict.py b/tests/test_split_on_grid_dict.py
index 5f3e442640..7b96fc4190 100644
--- a/tests/test_split_on_grid_dict.py
+++ b/tests/test_split_on_grid_dict.py
@@ -34,6 +34,11 @@
 TEST_CASE_5 = [{"keys": "image", "grid_size": 1, "patch_size": 4}, {"image": A}, torch.stack([A])]
 TEST_CASE_6 = [{"keys": "image", "grid_size": 2, "patch_size": 2}, {"image": A}, torch.stack([A11, A12, A21, A22])]
 TEST_CASE_7 = [{"keys": "image", "grid_size": 1}, {"image": A}, torch.stack([A])]
+TEST_CASE_8 = [
+    {"keys": "image", "grid_size": (2, 2), "patch_size": 2},
+    {"image": torch.arange(12).reshape(1, 3, 4).to(torch.float32)},
+    torch.Tensor([[[[0, 1], [4, 5]]], [[[2, 3], [6, 7]]], [[[4, 5], [8, 9]]], [[[6, 7], [10, 11]]]]).to(torch.float32),
+]
 
 TEST_SINGLE = []
 for p in TEST_NDARRAYS:
@@ -45,6 +50,7 @@
     TEST_SINGLE.append([p, *TEST_CASE_5])
     TEST_SINGLE.append([p, *TEST_CASE_6])
     TEST_SINGLE.append([p, *TEST_CASE_7])
+    TEST_SINGLE.append([p, *TEST_CASE_8])
 
 TEST_CASE_MC_0 = [
     {"keys": "image", "grid_size": (2, 2)},