diff --git a/python/cucim/src/cucim/core/operations/morphology/tests/test_distance_transform.py b/python/cucim/src/cucim/core/operations/morphology/tests/test_distance_transform.py
index 50b6335db..bf4a54400 100644
--- a/python/cucim/src/cucim/core/operations/morphology/tests/test_distance_transform.py
+++ b/python/cucim/src/cucim/core/operations/morphology/tests/test_distance_transform.py
@@ -6,6 +6,7 @@
 import scipy.ndimage as ndi_cpu
 
 from cucim.core.operations.morphology import distance_transform_edt
+from cucim.skimage._shared.compat import _full
 
 
 def binary_image(shape, pct_true=50):
@@ -137,7 +138,7 @@ def test_distance_transform_edt_block_params_invalid(block_params):
 @pytest.mark.parametrize("ndim", [2, 3])
 def test_distance_transform_edt_uniform_valued(value, ndim):
     """ensure default block_params is robust to anisotropic shape."""
-    img = cp.full((48,) * ndim, value, dtype=cp.uint8)
+    img = _full((48,) * ndim, value, dtype=cp.uint8)
     # ensure there is at least 1 pixel at background intensity
     img[(slice(24, 25),) * ndim] = 0
     out = distance_transform_edt(img)
diff --git a/python/cucim/src/cucim/skimage/_shared/compat.py b/python/cucim/src/cucim/skimage/_shared/compat.py
index eb17c6202..6ce9ab11d 100644
--- a/python/cucim/src/cucim/skimage/_shared/compat.py
+++ b/python/cucim/src/cucim/skimage/_shared/compat.py
@@ -1,5 +1,6 @@
 """Compatibility helpers for dependencies."""
 
+import cupy as cp
 import numpy as np
 from packaging.version import parse
 
@@ -28,3 +29,12 @@
 # deprecated in favor of `rtol`.
 # As of CuPy 13.0, it is still always using 'tol''
 SCIPY_CG_TOL_PARAM_NAME = "tol"  # if CUPY_LT_14 else "rtol"
+
+
+def _full(shape, fill_value, dtype=None, order="C"):
+    if NUMPY_LT_2_0_0:
+        return cp.full(shape, fill_value, dtype, order)
+    else:
+        out = cp.empty(shape, dtype=dtype, order=order)
+        out[:] = fill_value
+        return out
diff --git a/python/cucim/src/cucim/skimage/_shared/utils.py b/python/cucim/src/cucim/skimage/_shared/utils.py
index d5da200b6..64c5fa8e1 100644
--- a/python/cucim/src/cucim/skimage/_shared/utils.py
+++ b/python/cucim/src/cucim/skimage/_shared/utils.py
@@ -743,7 +743,7 @@ def check_random_state(seed):
     if seed is None or seed is cp.random:
         return cp.random.mtrand._rand
     if isinstance(seed, (numbers.Integral, cp.integer)):
-        return cp.random.RandomState(seed)
+        return cp.random.RandomState(cp.uint32(seed))
     if isinstance(seed, cp.random.RandomState):
         return seed
     raise ValueError(
diff --git a/python/cucim/src/cucim/skimage/_vendored/_ndimage_filters.py b/python/cucim/src/cucim/skimage/_vendored/_ndimage_filters.py
index b7e2ab4a3..1467ddc1b 100644
--- a/python/cucim/src/cucim/skimage/_vendored/_ndimage_filters.py
+++ b/python/cucim/src/cucim/skimage/_vendored/_ndimage_filters.py
@@ -5,6 +5,7 @@
 import cupy
 import numpy
 
+from cucim.skimage._shared.compat import _full
 from cucim.skimage._vendored import (
     _internal as internal,
     _ndimage_filters_core as _filters_core,
@@ -361,7 +362,7 @@ def uniform_filter1d(
         from SciPy due to floating-point rounding of intermediate results.
     """
     weights_dtype = cupy.promote_types(input.dtype, cupy.float32)
-    weights = cupy.full(size, 1 / size, dtype=weights_dtype)
+    weights = _full(size, 1 / size, dtype=weights_dtype)
     return correlate1d(
         input, weights, axis, output, mode, cval, origin, algorithm=algorithm
     )
@@ -410,9 +411,7 @@ def uniform_filter(
 
     def get(size):
         return (
-            None
-            if size <= 1
-            else cupy.full(size, 1 / size, dtype=weights_dtype)
+            None if size <= 1 else _full(size, 1 / size, dtype=weights_dtype)
         )  # noqa
 
     return _run_1d_correlates(
diff --git a/python/cucim/src/cucim/skimage/color/colorconv.py b/python/cucim/src/cucim/skimage/color/colorconv.py
index b7d33fc10..45bd5e189 100644
--- a/python/cucim/src/cucim/skimage/color/colorconv.py
+++ b/python/cucim/src/cucim/skimage/color/colorconv.py
@@ -55,6 +55,7 @@
 import numpy as np
 from scipy import linalg
 
+from .._shared.compat import _full
 from .._shared.utils import (
     _supported_float_type,
     channel_as_last_axis,
@@ -71,6 +72,9 @@
     from numpy.exceptions import AxisError
 
 
+using_numpy2 = np.__version__.split(".")[0] == 2
+
+
 def convert_colorspace(arr, fromspace, tospace, *, channel_axis=-1):
     """Convert an image array to a new color space.
 
@@ -1131,7 +1135,7 @@ def gray2rgba(image, alpha=None, *, channel_axis=-1, check_alpha=True):
                     f'{image.dtype.name}',
                     stacklevel=2
                 )
-        alpha_arr = cp.full(image.shape, alpha, dtype=image.dtype)
+        alpha_arr = _full(image.shape, alpha, dtype=image.dtype)
     else:
         alpha_arr = cp.asarray(alpha).astype(image.dtype, copy=False)
         if check_alpha and not cp.array_equal(alpha, alpha_arr):
diff --git a/python/cucim/src/cucim/skimage/color/tests/test_colorlabel.py b/python/cucim/src/cucim/skimage/color/tests/test_colorlabel.py
index 281c1b1e6..21a187674 100644
--- a/python/cucim/src/cucim/skimage/color/tests/test_colorlabel.py
+++ b/python/cucim/src/cucim/skimage/color/tests/test_colorlabel.py
@@ -6,9 +6,12 @@
 from cupy.testing import assert_array_almost_equal, assert_array_equal
 from numpy.testing import assert_no_warnings
 
+from cucim.skimage._shared.compat import _full
 from cucim.skimage._shared.testing import expected_warnings
 from cucim.skimage.color.colorlabel import hsv2rgb, label2rgb, rgb2hsv
 
+using_numpy2 = np.__version__.split(".")[0] == 2
+
 
 def test_shape_mismatch():
     image = cp.ones((3, 3))
@@ -213,7 +216,7 @@ def test_avg(channel_axis):
 
 def test_negative_intensity():
     labels = cp.arange(100).reshape(10, 10)
-    image = cp.full((10, 10), -1, dtype="float64")
+    image = _full((10, 10), -1, dtype="float64")
     with pytest.warns(UserWarning):
         label2rgb(labels, image, bg_label=-1)
 
diff --git a/python/cucim/src/cucim/skimage/feature/tests/test_canny.py b/python/cucim/src/cucim/skimage/feature/tests/test_canny.py
index 80beb4fc8..286410007 100644
--- a/python/cucim/src/cucim/skimage/feature/tests/test_canny.py
+++ b/python/cucim/src/cucim/skimage/feature/tests/test_canny.py
@@ -51,7 +51,7 @@ def test_01_01_circle(self):
     def test_01_02_circle_with_noise(self):
         """Test that the Canny filter finds the circle outlines
         in a noisy image"""
-        cp.random.seed(0)
+        cp.random.seed(cp.uint32(0))
         i, j = cp.mgrid[-200:200, -200:200].astype(float) / 200
         c = cp.abs(cp.sqrt(i * i + j * j) - 0.5) < 0.02
         cf = c.astype(float) * 0.5 + cp.random.uniform(size=c.shape) * 0.5
diff --git a/python/cucim/src/cucim/skimage/feature/tests/test_peak.py b/python/cucim/src/cucim/skimage/feature/tests/test_peak.py
index 58b5e490d..dfa14ab3e 100644
--- a/python/cucim/src/cucim/skimage/feature/tests/test_peak.py
+++ b/python/cucim/src/cucim/skimage/feature/tests/test_peak.py
@@ -8,6 +8,7 @@
 from scipy import ndimage as ndimage_cpu
 
 from cucim.skimage._shared._warnings import expected_warnings
+from cucim.skimage._shared.compat import _full
 from cucim.skimage.feature import peak
 
 np.random.seed(21)
@@ -51,7 +52,7 @@ def test_absolute_threshold(self):
         assert_array_almost_equal(peaks, [(3, 3)])
 
     def test_constant_image(self):
-        image = cp.full((20, 20), 128, dtype=cp.uint8)
+        image = _full((20, 20), 128, dtype=cp.uint8)
         peaks = peak.peak_local_max(image, min_distance=1)
         assert len(peaks) == 0
 
@@ -491,7 +492,7 @@ def test_threshold_rel_default(self):
             )
 
     def test_peak_at_border(self):
-        image = cp.full((10, 10), -2)
+        image = _full((10, 10), -2)
         image[2, 4] = -1
         image[3, 0] = -1
 
diff --git a/python/cucim/src/cucim/skimage/feature/tests/test_template.py b/python/cucim/src/cucim/skimage/feature/tests/test_template.py
index 186b829e3..4253c6307 100644
--- a/python/cucim/src/cucim/skimage/feature/tests/test_template.py
+++ b/python/cucim/src/cucim/skimage/feature/tests/test_template.py
@@ -6,6 +6,7 @@
 from skimage import data
 
 from cucim.skimage import img_as_float
+from cucim.skimage._shared.compat import _full
 from cucim.skimage.feature import match_template, peak_local_max
 from cucim.skimage.morphology import diamond
 
@@ -55,7 +56,7 @@ def test_normalization():
     N = 20
     ipos, jpos = (2, 3)
     ineg, jneg = (12, 11)
-    image = cp.full((N, N), 0.5)
+    image = _full((N, N), 0.5)
     image[ipos : ipos + n, jpos : jpos + n] = 1
     image[ineg : ineg + n, jneg : jneg + n] = 0
 
diff --git a/python/cucim/src/cucim/skimage/filters/tests/test_gabor.py b/python/cucim/src/cucim/skimage/filters/tests/test_gabor.py
index 7f3a8c4f2..72d050646 100644
--- a/python/cucim/src/cucim/skimage/filters/tests/test_gabor.py
+++ b/python/cucim/src/cucim/skimage/filters/tests/test_gabor.py
@@ -4,6 +4,7 @@
 from cupy.testing import assert_array_almost_equal
 from numpy.testing import assert_almost_equal
 
+from cucim.skimage._shared.compat import _full
 from cucim.skimage._shared.utils import _supported_float_type
 from cucim.skimage.filters._gabor import _sigma_prefactor, gabor, gabor_kernel
 
@@ -117,6 +118,6 @@ def test_gabor_float_dtype(dtype):
 
 @pytest.mark.parametrize("dtype", [cp.uint8, cp.int32, cp.intp])
 def test_gabor_int_dtype(dtype):
-    image = cp.full((16, 16), 128, dtype=dtype)
+    image = _full((16, 16), 128, dtype=dtype)
     y = gabor(image, 0.3)
     assert all(arr.dtype == dtype for arr in y)
diff --git a/python/cucim/src/cucim/skimage/filters/tests/test_thresholding.py b/python/cucim/src/cucim/skimage/filters/tests/test_thresholding.py
index 3825dcb7e..609782907 100644
--- a/python/cucim/src/cucim/skimage/filters/tests/test_thresholding.py
+++ b/python/cucim/src/cucim/skimage/filters/tests/test_thresholding.py
@@ -17,6 +17,7 @@
 from cucim.skimage import util
 from cucim.skimage._shared._dependency_checks import has_mpl
 from cucim.skimage._shared._warnings import expected_warnings
+from cucim.skimage._shared.compat import _full
 from cucim.skimage._shared.utils import _supported_float_type
 from cucim.skimage.color import rgb2gray
 from cucim.skimage.exposure import histogram
@@ -388,7 +389,7 @@ def test_li_astro_image():
 
 
 def test_li_nan_image():
-    image = cp.full((5, 5), cp.nan)
+    image = _full((5, 5), cp.nan)
     assert cp.isnan(threshold_li(image))
 
 
@@ -639,7 +640,7 @@ def test_mean():
 def test_triangle_uniform_images(dtype, kwargs):
     assert threshold_triangle(cp.zeros((10, 10), dtype=dtype), **kwargs) == 0
     assert threshold_triangle(cp.ones((10, 10), dtype=dtype), **kwargs) == 1
-    assert threshold_triangle(cp.full((10, 10), 2, dtype=dtype), **kwargs) == 2
+    assert threshold_triangle(_full((10, 10), 2, dtype=dtype), **kwargs) == 2
 
 
 # also run cases with nbins > 100000 to also test CuPy-based code path.
@@ -775,7 +776,7 @@ def test_niblack_sauvola_pathological_image():
     # resulted in NaNs. Here we check that these are safely caught.
     # see https://github.com/scikit-image/scikit-image/issues/3007
     value = 0.03082192 + 2.19178082e-09
-    src_img = cp.full((4, 4), value).astype(cp.float64)
+    src_img = _full((4, 4), value).astype(cp.float64)
     assert not cp.any(cp.isnan(threshold_niblack(src_img)))
 
 
diff --git a/python/cucim/src/cucim/skimage/measure/_moments.py b/python/cucim/src/cucim/skimage/measure/_moments.py
index 2c52b227f..2ea147ec2 100644
--- a/python/cucim/src/cucim/skimage/measure/_moments.py
+++ b/python/cucim/src/cucim/skimage/measure/_moments.py
@@ -3,6 +3,7 @@
 import cupy as cp
 import numpy as np
 
+from .._shared.compat import _full
 from .._shared.utils import _supported_float_type, check_nD
 from ._moments_analytical import moments_raw_to_central
 
@@ -420,7 +421,7 @@ def moments_normalized(mu, order=3, spacing=None):
     # compute using in a single kernel for the 2D or 3D cases
     unit_scale = scale == 1.0
     kernel = _get_normalize_kernel(mu.ndim, order, unit_scale)
-    nu = cp.full(mu.shape, cp.nan, dtype=mu.dtype)
+    nu = _full(mu.shape, cp.nan, dtype=mu.dtype)
     kernel(mu, order, scale, nu)
     return nu
 
diff --git a/python/cucim/src/cucim/skimage/metrics/_contingency_table.py b/python/cucim/src/cucim/skimage/metrics/_contingency_table.py
index 45923b478..e446bc176 100644
--- a/python/cucim/src/cucim/skimage/metrics/_contingency_table.py
+++ b/python/cucim/src/cucim/skimage/metrics/_contingency_table.py
@@ -1,6 +1,8 @@
 import cupy as cp
 import cupyx.scipy.sparse as sparse
 
+from .._shared.compat import _full
+
 __all__ = ["contingency_table"]
 
 
@@ -36,7 +38,7 @@ def contingency_table(im_true, im_test, *, ignore_labels=None, normalize=False):
             data /= cp.count_nonzero(data)
     else:
         if normalize:
-            data = cp.full((im_test_r.size,), 1 / im_test_r.size, dtype=float)
+            data = _full((im_test_r.size,), 1 / im_test_r.size, dtype=float)
         else:
             data = cp.ones((im_test_r.size,), dtype=float)
     cont = sparse.coo_matrix((data, (im_true_r, im_test_r))).tocsr()
diff --git a/python/cucim/src/cucim/skimage/metrics/tests/test_structural_similarity.py b/python/cucim/src/cucim/skimage/metrics/tests/test_structural_similarity.py
index 6a64a3b7e..778d46e2f 100644
--- a/python/cucim/src/cucim/skimage/metrics/tests/test_structural_similarity.py
+++ b/python/cucim/src/cucim/skimage/metrics/tests/test_structural_similarity.py
@@ -16,7 +16,7 @@
 cam_noisy = cam_noisy.astype(cam.dtype)
 
 
-cp.random.seed(1234)
+cp.random.seed(cp.uint32(1234))
 
 assert_equal = cp.testing.assert_array_equal
 assert_almost_equal = cp.testing.assert_array_almost_equal
@@ -25,7 +25,7 @@
 
 def test_structural_similarity_patch_range():
     N = 51
-    rstate = cp.random.RandomState(1234)
+    rstate = cp.random.RandomState(cp.uint32(1234))
     X = (rstate.rand(N, N) * 255).astype(cp.uint8)
     Y = (rstate.rand(N, N) * 255).astype(cp.uint8)
 
@@ -35,7 +35,7 @@ def test_structural_similarity_patch_range():
 
 def test_structural_similarity_image():
     N = 100
-    rstate = cp.random.RandomState(1234)
+    rstate = cp.random.RandomState(cp.uint32(1234))
     X = (rstate.rand(N, N) * 255).astype(cp.uint8)
     Y = (rstate.rand(N, N) * 255).astype(cp.uint8)
 
diff --git a/python/cucim/src/cucim/skimage/morphology/_skeletonize.py b/python/cucim/src/cucim/skimage/morphology/_skeletonize.py
index 4abe6c1c7..350ffe52f 100644
--- a/python/cucim/src/cucim/skimage/morphology/_skeletonize.py
+++ b/python/cucim/src/cucim/skimage/morphology/_skeletonize.py
@@ -159,6 +159,9 @@ def thin(image, max_num_iter=None):
 def _get_tiebreaker(n, seed):
     # CuPy generator doesn't currently have the permutation method, so
     # fall back to cp.random.permutation instead.
+    if np.isscalar(seed):
+        # TODO: remove this NumPy 2.0 compat. fix once CUPy 13.3 is released
+        seed = cp.uint32(seed)
     cp.random.seed(seed)
     if n < 2 << 31:
         dtype = np.int32
diff --git a/python/cucim/src/cucim/skimage/morphology/grayreconstruct.py b/python/cucim/src/cucim/skimage/morphology/grayreconstruct.py
index e94dbcbec..266ce29b7 100644
--- a/python/cucim/src/cucim/skimage/morphology/grayreconstruct.py
+++ b/python/cucim/src/cucim/skimage/morphology/grayreconstruct.py
@@ -14,6 +14,8 @@
 import skimage
 from packaging.version import Version
 
+from .._shared.compat import _full
+
 old_reconstruction_pyx = Version(skimage.__version__) < Version("0.20.0")
 
 
@@ -190,7 +192,7 @@ def reconstruction(seed, mask, method="dilation", footprint=None, offset=None):
     # CuPy Backend: modified to allow images_dtype based on input dtype
     #               instead of float64
     images_dtype = np.promote_types(seed.dtype, mask.dtype)
-    images = cp.full(dims, pad_value, dtype=images_dtype)
+    images = _full(dims, pad_value, dtype=images_dtype)
     images[(0, *inside_slices)] = seed
     images[(1, *inside_slices)] = mask
     isize = images.size
diff --git a/python/cucim/src/cucim/skimage/morphology/tests/test_skeletonize.py b/python/cucim/src/cucim/skimage/morphology/tests/test_skeletonize.py
index 03c012e60..8a345c7bf 100644
--- a/python/cucim/src/cucim/skimage/morphology/tests/test_skeletonize.py
+++ b/python/cucim/src/cucim/skimage/morphology/tests/test_skeletonize.py
@@ -5,6 +5,7 @@
 from skimage import data
 from skimage.morphology import thin as thin_cpu
 
+from cucim.skimage._shared.compat import _full
 from cucim.skimage.morphology import medial_axis, thin
 
 
@@ -99,7 +100,7 @@ def _test_vertical_line(self, dtype, **kwargs):
         img[:, 3] = 2
         img[:, 4] = 3
 
-        expected = cp.full(img.shape, False)
+        expected = _full(img.shape, False)
         expected[:, 3] = True
 
         result = medial_axis(img, **kwargs)
diff --git a/python/cucim/src/cucim/skimage/registration/_optical_flow.py b/python/cucim/src/cucim/skimage/registration/_optical_flow.py
index 6d115ac20..7a661582c 100644
--- a/python/cucim/src/cucim/skimage/registration/_optical_flow.py
+++ b/python/cucim/src/cucim/skimage/registration/_optical_flow.py
@@ -10,6 +10,7 @@
 
 from .._shared._gradient import gradient
 from .._shared.utils import _supported_float_type
+from .._vendored.ndimage import uniform_filter  # has numpy 2.0 compat fix
 from ..transform import warp
 from ._optical_flow_utils import _coarse_to_fine, _get_warp_points
 
@@ -303,7 +304,7 @@ def _ilk(
         filter_func = partial(gaussian_filter, sigma=sigma, mode="mirror")
     else:
         filter_func = partial(
-            ndi.uniform_filter, size=ndim * (size,), mode="mirror"
+            uniform_filter, size=ndim * (size,), mode="mirror"
         )
 
     flow = flow0
diff --git a/python/cucim/src/cucim/skimage/registration/tests/test_tvl1.py b/python/cucim/src/cucim/skimage/registration/tests/test_tvl1.py
index 20a70b039..4a6985698 100644
--- a/python/cucim/src/cucim/skimage/registration/tests/test_tvl1.py
+++ b/python/cucim/src/cucim/skimage/registration/tests/test_tvl1.py
@@ -41,7 +41,7 @@ def _sin_flow_gen(image0, max_motion=4.5, npics=5):
 @pytest.mark.parametrize("dtype", [cp.float16, cp.float32, cp.float64])
 def test_2d_motion(dtype):
     # Generate synthetic data
-    rnd = cp.random.RandomState(0)
+    rnd = cp.random.RandomState(cp.uint32(0))
     image0 = cp.array(rnd.normal(size=(256, 256)).astype(dtype))
     gt_flow, image1 = _sin_flow_gen(image0)
     image1 = image1.astype(dtype, copy=False)
diff --git a/python/cucim/src/cucim/skimage/restoration/deconvolution.py b/python/cucim/src/cucim/skimage/restoration/deconvolution.py
index 41572359b..cbf21bdb5 100644
--- a/python/cucim/src/cucim/skimage/restoration/deconvolution.py
+++ b/python/cucim/src/cucim/skimage/restoration/deconvolution.py
@@ -4,6 +4,7 @@
 import cupy as cp
 import numpy as np
 
+from .._shared.compat import _full
 from .._shared.utils import (
     DEPRECATED,
     _supported_float_type,
@@ -456,7 +457,7 @@ def richardson_lucy(image, psf, num_iter=50, clip=True, filter_epsilon=None):
     float_type = _supported_float_type(image.dtype)
     image = image.astype(float_type, copy=False)
     psf = psf.astype(float_type, copy=False)
-    im_deconv = cp.full(image.shape, 0.5, dtype=float_type)
+    im_deconv = _full(image.shape, 0.5, dtype=float_type)
     psf_mirror = cp.ascontiguousarray(psf[::-1, ::-1])
 
     # Small regularization parameter used to avoid 0 divisions
diff --git a/python/cucim/src/cucim/skimage/restoration/tests/test_denoise.py b/python/cucim/src/cucim/skimage/restoration/tests/test_denoise.py
index ae281a634..b907b0cbf 100644
--- a/python/cucim/src/cucim/skimage/restoration/tests/test_denoise.py
+++ b/python/cucim/src/cucim/skimage/restoration/tests/test_denoise.py
@@ -10,7 +10,7 @@
 from cucim.skimage._shared.utils import _supported_float_type, slice_at_axis
 from cucim.skimage.metrics import structural_similarity
 
-cp.random.seed(1234)
+cp.random.seed(cp.uint32(1234))
 
 
 astro = img_as_float(data.astronaut()[:128, :128])
@@ -140,7 +140,7 @@ def test_denoise_tv_chambolle_4d():
 def test_denoise_tv_chambolle_weighting():
     # make sure a specified weight gives consistent results regardless of
     # the number of input image dimensions
-    rstate = cp.random.RandomState(1234)
+    rstate = cp.random.RandomState(cp.uint32(1234))
     img2d = astro_gray.copy()
     img2d += 0.15 * rstate.standard_normal(img2d.shape)
     img2d = cp.clip(img2d, 0, 1)
diff --git a/python/cucim/src/cucim/skimage/segmentation/boundaries.py b/python/cucim/src/cucim/skimage/segmentation/boundaries.py
index 7bdfb24ad..90c5ac8cb 100644
--- a/python/cucim/src/cucim/skimage/segmentation/boundaries.py
+++ b/python/cucim/src/cucim/skimage/segmentation/boundaries.py
@@ -2,6 +2,7 @@
 
 import cucim.skimage._vendored.ndimage as ndi
 
+from .._shared.compat import _full
 from .._shared.utils import _supported_float_type
 from ..color import gray2rgb
 from ..morphology import dilation, erosion, square
@@ -26,7 +27,7 @@ def _find_boundaries_subpixel(label_img):
     ndim = label_img.ndim
     max_label = cp.iinfo(label_img.dtype).max
 
-    label_img_expanded = cp.full(
+    label_img_expanded = _full(
         [(2 * s - 1) for s in label_img.shape], max_label, label_img.dtype
     )
     pixels = (slice(None, None, 2),) * ndim
diff --git a/python/cucim/src/cucim/skimage/segmentation/random_walker_segmentation.py b/python/cucim/src/cucim/skimage/segmentation/random_walker_segmentation.py
index cc3ff3e65..c2dea2b50 100644
--- a/python/cucim/src/cucim/skimage/segmentation/random_walker_segmentation.py
+++ b/python/cucim/src/cucim/skimage/segmentation/random_walker_segmentation.py
@@ -434,7 +434,7 @@ def random_walker(
     Examples
     --------
     >>> import cupy as cp
-    >>> cp.random.seed(0)
+    >>> cp.random.seed(cp.uint32(0))
     >>> a = cp.zeros((10, 10)) + 0.2 * cp.random.rand(10, 10)
     >>> a[5:8, 5:8] += 1
     >>> b = cp.zeros_like(a, dtype=cp.int32)
diff --git a/python/cucim/src/cucim/skimage/segmentation/tests/test_chan_vese.py b/python/cucim/src/cucim/skimage/segmentation/tests/test_chan_vese.py
index ec1f61f06..2b2ec0913 100644
--- a/python/cucim/src/cucim/skimage/segmentation/tests/test_chan_vese.py
+++ b/python/cucim/src/cucim/skimage/segmentation/tests/test_chan_vese.py
@@ -2,6 +2,7 @@
 import pytest
 from cupy.testing import assert_array_equal
 
+from cucim.skimage._shared.compat import _full
 from cucim.skimage._shared.utils import _supported_float_type
 from cucim.skimage.segmentation import chan_vese
 
@@ -16,7 +17,7 @@ def test_chan_vese_flat_level_set(dtype):
     # infinite time, the segmentation will still converge.
     img = cp.zeros((10, 10), dtype=dtype)
     img[3:6, 3:6] = 1
-    ls = cp.full((10, 10), 1000, dtype=dtype)
+    ls = _full((10, 10), 1000, dtype=dtype)
     result = chan_vese(img, mu=0.0, tol=1e-3, init_level_set=ls)
     assert_array_equal(result.astype(float), cp.ones((10, 10)))
     result = chan_vese(img, mu=0.0, tol=1e-3, init_level_set=-ls)
diff --git a/python/cucim/src/cucim/skimage/segmentation/tests/test_clear_border.py b/python/cucim/src/cucim/skimage/segmentation/tests/test_clear_border.py
index aa70b185e..4aaf3fc4d 100644
--- a/python/cucim/src/cucim/skimage/segmentation/tests/test_clear_border.py
+++ b/python/cucim/src/cucim/skimage/segmentation/tests/test_clear_border.py
@@ -1,6 +1,7 @@
 import cupy as cp
 from cupy.testing import assert_array_equal
 
+from cucim.skimage._shared.compat import _full
 from cucim.skimage.segmentation import clear_border
 
 
@@ -29,7 +30,7 @@ def test_clear_border():
 
     # test background value
     result = clear_border(image.copy(), buffer_size=1, bgval=2)
-    assert_array_equal(result, cp.full_like(image, 2))
+    assert_array_equal(result, _full(image.shape, 2, dtype=image.dtype))
 
     # test mask
     mask = cp.array(
@@ -77,12 +78,14 @@ def test_clear_border_3d():
 
     # test background value
     result = clear_border(image.copy(), buffer_size=1, bgval=2)
-    assert_array_equal(result, cp.full_like(image, 2))
+    assert_array_equal(result, _full(image.shape, 2, dtype=image.dtype))
 
     # test floating-point background value
     image_f32 = image.astype(cp.float32)
     result = clear_border(image_f32, buffer_size=1, bgval=2.5)
-    assert_array_equal(result, cp.full_like(image_f32, 2.5))
+    assert_array_equal(
+        result, _full(image_f32.shape, 2.5, dtype=image_f32.dtype)
+    )
 
 
 def test_clear_border_non_binary():
diff --git a/python/cucim/src/cucim/skimage/segmentation/tests/test_random_walker.py b/python/cucim/src/cucim/skimage/segmentation/tests/test_random_walker.py
index efcae02c5..b07494fa4 100644
--- a/python/cucim/src/cucim/skimage/segmentation/tests/test_random_walker.py
+++ b/python/cucim/src/cucim/skimage/segmentation/tests/test_random_walker.py
@@ -6,6 +6,7 @@
 
 from cucim.skimage._shared import testing
 from cucim.skimage._shared._warnings import expected_warnings
+from cucim.skimage._shared.compat import _full
 from cucim.skimage.segmentation import random_walker
 from cucim.skimage.transform import resize
 
@@ -364,7 +365,7 @@ def test_trivial_cases():
     cp.testing.assert_array_equal(test, expected)
 
     # Unlabeled voxels not connected to seed, so nothing can be done
-    img = cp.full((10, 10), False)
+    img = _full((10, 10), False)
     object_A = np.array([(6, 7), (6, 8), (7, 7), (7, 8)])
     object_B = np.array(
         [(3, 1), (4, 1), (2, 2), (3, 2), (4, 2), (2, 3), (3, 3)]
@@ -389,7 +390,7 @@ def test_trivial_cases():
 def test_length2_spacing():
     # If this passes without raising an exception (warnings OK), the new
     #   spacing code is working properly.
-    cp.random.seed(42)
+    cp.random.seed(cp.uint32(42))
     img = cp.ones((10, 10)) + 0.2 * cp.random.normal(size=(10, 10))
     labels = cp.zeros((10, 10), dtype=cp.uint8)
     labels[2, 4] = 1
diff --git a/python/cucim/src/cucim/skimage/transform/_geometric.py b/python/cucim/src/cucim/skimage/transform/_geometric.py
index 25347db26..83019d4e9 100644
--- a/python/cucim/src/cucim/skimage/transform/_geometric.py
+++ b/python/cucim/src/cucim/skimage/transform/_geometric.py
@@ -7,7 +7,7 @@
 import numpy as np
 from scipy import spatial
 
-from .._shared.compat import NP_COPY_IF_NEEDED
+from .._shared.compat import NP_COPY_IF_NEEDED, _full
 from .._shared.utils import get_bound_method_class, safe_as_int
 
 _sin, _cos = math.sin, math.cos
@@ -80,9 +80,10 @@ def _center_and_normalize_points(points):
     # small value; ie, we don't need to worry about numerical stability here,
     # only actual 0.
     if rms == 0:
+        full_func = xp.full if xp == np else _full
         return (
-            xp.full((d + 1, d + 1), xp.nan),
-            xp.full_like(points, xp.nan),
+            full_func((d + 1, d + 1), xp.nan),
+            full_func(points.shape, xp.nan, dtype=points.dtype),
             True,
         )
 
@@ -411,17 +412,18 @@ def _setup_constraint_matrix(self, src, dst):
             raise ValueError("src.shape[0] must be equal or larger than 8.")
         xp = cp.get_array_module(src)
 
+        full_func = xp.full if xp == np else _full
         # Center and normalize image points for better numerical stability.
         try:
             src_matrix, src, has_nan1 = _center_and_normalize_points(src)
             dst_matrix, dst, has_nan2 = _center_and_normalize_points(dst)
         except ZeroDivisionError:
-            self.params = xp.full((3, 3), xp.nan)
-            return 3 * [xp.full((3, 3), xp.nan)]
+            self.params = full_func((3, 3), xp.nan)
+            return 3 * [full_func((3, 3), xp.nan)]
 
         if has_nan1 or has_nan2:
-            self.params = xp.full((3, 3), xp.nan)
-            return 3 * [xp.full((3, 3), xp.nan)]
+            self.params = full_func((3, 3), xp.nan)
+            return 3 * [full_func((3, 3), xp.nan)]
 
         # Setup homogeneous linear equation as dst' * F * src = 0.
         A = xp.ones((src.shape[0], 9))
@@ -848,8 +850,9 @@ def estimate(self, src, dst, weights=None):
         n, d = src.shape
         src_matrix, src, has_nan1 = _center_and_normalize_points(src)
         dst_matrix, dst, has_nan2 = _center_and_normalize_points(dst)
+        full_func = xp.full if xp == np else _full
         if has_nan1 or has_nan2:
-            self.params = xp.full((d + 1, d + 1), xp.nan)
+            self.params = full_func((d + 1, d + 1), xp.nan)
             return False
         # params: a0, a1, a2, b0, b1, b2, c0, c1
         A = xp.zeros((n * d, (d + 1) ** 2))
@@ -878,7 +881,7 @@ def estimate(self, src, dst, weights=None):
         # because it is a rank-defective transform, which would map points
         # to a line rather than a plane.
         if xp.isclose(V[-1, -1], 0):
-            self.params = xp.full((d + 1, d + 1), xp.nan)
+            self.params = full_func((d + 1, d + 1), xp.nan)
             return False
 
         H = np.zeros(
diff --git a/python/cucim/src/cucim/skimage/transform/tests/test_integral.py b/python/cucim/src/cucim/skimage/transform/tests/test_integral.py
index 0010b2b03..5c6ab55ea 100644
--- a/python/cucim/src/cucim/skimage/transform/tests/test_integral.py
+++ b/python/cucim/src/cucim/skimage/transform/tests/test_integral.py
@@ -5,7 +5,7 @@
 
 from cucim.skimage.transform import integral_image, integrate
 
-cp.random.seed(0)
+cp.random.seed(cp.uint32(0))
 x = (cp.random.rand(50, 50) * 255).astype(np.uint8)
 s = integral_image(x)
 
diff --git a/python/cucim/src/cucim/skimage/transform/tests/test_warps.py b/python/cucim/src/cucim/skimage/transform/tests/test_warps.py
index 643663a5c..da0950832 100644
--- a/python/cucim/src/cucim/skimage/transform/tests/test_warps.py
+++ b/python/cucim/src/cucim/skimage/transform/tests/test_warps.py
@@ -41,7 +41,7 @@
 # from skimage._shared.testing import test_parallel
 
 
-cp.random.seed(0)
+cp.random.seed(cp.uint32(0))
 
 
 def test_stackcopy():
diff --git a/python/cucim/tests/unit/core/test_stain_normalizer.py b/python/cucim/tests/unit/core/test_stain_normalizer.py
index 34a44384c..086784496 100644
--- a/python/cucim/tests/unit/core/test_stain_normalizer.py
+++ b/python/cucim/tests/unit/core/test_stain_normalizer.py
@@ -20,17 +20,18 @@
     normalize_colors_pca,
     stain_extraction_pca,
 )
+from cucim.skimage._shared.compat import _full
 
 
 class TestStainExtractorMacenko:
     @pytest.mark.parametrize(
         "image, ErrorClass",
         [
-            (cp.full((3, 2, 4), -1), ValueError),  # negative value
-            (cp.full((3, 2, 4), 256), ValueError),  # out of range value
+            (_full((3, 2, 4), -1), ValueError),  # negative value
+            (_full((3, 2, 4), 256), ValueError),  # out of range value
             (None, TypeError),
             (
-                cp.full((3, 2, 4), 240),
+                _full((3, 2, 4), 240),
                 ValueError,
             ),  # uniformly below the beta threshold  # noqa
         ],
@@ -50,8 +51,8 @@ def test_transparent_image(self, image, ErrorClass):
         "image",
         [
             None,
-            cp.full((3, 2, 4), 100),  # uniform, above beta absorbance thresh.
-            cp.full((3, 2, 4), 150),  # uniform, above beta absorbance thresh.
+            _full((3, 2, 4), 100),  # uniform, above beta absorbance thresh.
+            _full((3, 2, 4), 150),  # uniform, above beta absorbance thresh.
         ],
     )
     def test_identical_result_vectors(self, image):
@@ -144,10 +145,10 @@ class TestStainNormalizerMacenko:
     @pytest.mark.parametrize(
         "image",
         [
-            cp.full((3, 2, 4), -1),  # negative value case
-            cp.full((3, 2, 4), 256),  # out of range value
+            _full((3, 2, 4), -1),  # negative value case
+            _full((3, 2, 4), 256),  # out of range value
             None,
-            cp.full((3, 2, 5), 240),  # uniformly below the beta threshold
+            _full((3, 2, 5), 240),  # uniformly below the beta threshold
         ],
     )
     def test_transparent_image(self, image):
@@ -185,9 +186,9 @@ def test_transparent_image(self, image):
             #   and finally converting to uint8, we get that the stain
             #   normalized image should be 12 everywhere.
             [
-                {"ref_stain_coeff": cp.full((3, 2), 1)},
+                {"ref_stain_coeff": _full((3, 2), 1)},
                 cp.zeros((3, 2, 4)),
-                cp.full((3, 2, 4), 12),
+                _full((3, 2, 4), 12),
             ],
             # 3.) input uniformly zero, and target stain matrix provided.
             # - As in test case 2, the normalized concentration matrix should
@@ -223,7 +224,7 @@ def test_transparent_image(self, image):
             #   the absorbance image, and finally converting to uint8, we get
             #   the expected result listed here.
             [
-                {"ref_stain_coeff": cp.full((3, 2), 1)},
+                {"ref_stain_coeff": _full((3, 2), 1)},
                 cp.array(
                     [
                         [[100, 0, 0], [0, 0, 0]],