diff --git a/auglab/configs/transform_params_gpu.json b/auglab/configs/transform_params_gpu.json
index 291b652..eda598e 100644
--- a/auglab/configs/transform_params_gpu.json
+++ b/auglab/configs/transform_params_gpu.json
@@ -43,6 +43,8 @@
     "RedistributeSegTransform": {
         "in_seg": 0.25,
         "retain_stats": true,
+        "std_noise_range": [0.1, 0.3],
+        "dilation_iterations_range": [1, 5],
         "probability": 0.4
     },
     "GaussianNoiseTransform": {
diff --git a/auglab/transforms/gpu/fromSeg.py b/auglab/transforms/gpu/fromSeg.py
index 83b3b90..7a1fc82 100644
--- a/auglab/transforms/gpu/fromSeg.py
+++ b/auglab/transforms/gpu/fromSeg.py
@@ -29,12 +29,16 @@ def __init__(
         same_on_batch: bool = False,
         p: float = 1.0,
         keepdim: bool = True,
+        std_noise_range: list[float] = [0.1, 0.3],
+        dilation_iterations_range: list[int] = [1, 3],
         **kwargs,
     ) -> None:
         super().__init__(p=p, same_on_batch=same_on_batch, keepdim=keepdim)
         self.in_seg = in_seg
         self.apply_to_channel = apply_to_channel
         self.retain_stats = retain_stats
+        self.std_noise_range = std_noise_range
+        self.dilation_iterations_range = dilation_iterations_range
 
     @torch.no_grad()
     def apply_transform(
@@ -96,7 +100,8 @@ def apply_transform(
 
                 # Vectorized dilation for all regions (3 iterations)
                 dilated = masks.float()
-                for _ in range(3):
+                dilation_iterations = torch.randint(self.dilation_iterations_range[0], self.dilation_iterations_range[1]+1, (1,), device=input.device)[0].item()
+                for _ in range(dilation_iterations):
                     if spatial_dims == 3:
                         dilated = F.max_pool3d(dilated.unsqueeze(0), 3, 1, 1).squeeze(0)
                     else:
@@ -125,8 +130,9 @@ def apply_transform(
                 dil_stds = dil_vars.sqrt()
 
                 # redist_std per region
+                std_noise_range = torch.rand(1, device=input.device)[0] * (self.std_noise_range[1] - self.std_noise_range[0]) + self.std_noise_range[0]
                 redist_std = torch.maximum(
-                    torch.rand(R, device=input.device) * 0.2 + 0.4 * torch.abs((means - dil_means) * stds / (dil_stds + 1e-6)),
+                    torch.rand(R, device=input.device) * std_noise_range + 0.4 * torch.abs((means - dil_means) * stds / (dil_stds + 1e-6)),
                     torch.full((R,), 0.01, device=input.device, dtype=input.dtype)
                 )
 
diff --git a/auglab/transforms/gpu/transforms.py b/auglab/transforms/gpu/transforms.py
index f471370..6e61cc6 100644
--- a/auglab/transforms/gpu/transforms.py
+++ b/auglab/transforms/gpu/transforms.py
@@ -93,6 +93,8 @@ def _build_transforms(self) -> list[nn.Module]:
                 in_seg=redistribute_params.get('in_seg', 0.2),
                 retain_stats=redistribute_params.get('retain_stats', False),
                 p=redistribute_params.get('probability', 0),
+                std_noise_range=redistribute_params.get('std_noise_range', [0.1, 0.3]),
+                dilation_iterations_range=redistribute_params.get('dilation_iterations_range', [1, 3]),
             ))
 
         # Scharr filter
@@ -395,22 +397,22 @@ def pad_numpy_array(arr, shape):
     augmentor = AugTransformsGPU(json_path)
 
     # Load images and masks tensors
-    img_path = '/home/GRAMES.POLYMTL.CA/p118739/data_nvme_p118739/data/datasets/data-multi-subject/sub-amu02/anat/sub-amu02_T1w.nii.gz'
+    img_path = '/home/ge.polymtl.ca/p118739/data/datasets/data-multi-subject/sub-amu02/anat/sub-amu02_T1w.nii.gz'
     img = Image(img_path).change_orientation('RSP')
     img = resample_nib(img, new_size=[1,1,1], new_size_type='mm', interpolation='linear')
     img_tensor = torch.from_numpy(img.data.copy()).to(torch.float32)
 
-    seg_path = '/home/GRAMES.POLYMTL.CA/p118739/data_nvme_p118739/data/datasets/data-multi-subject/derivatives/labels/sub-amu02/anat/sub-amu02_T1w_label-spine_dseg.nii.gz'
+    seg_path = '/home/ge.polymtl.ca/p118739/data/datasets/data-multi-subject/derivatives/labels/sub-amu02/anat/sub-amu02_T1w_label-spine_dseg.nii.gz'
     seg = Image(seg_path).change_orientation('RSP')
     seg = resample_nib(seg, new_size=[1,1,1], new_size_type='mm', interpolation='nn')
     seg_tensor_all = torch.from_numpy(seg.data.copy())
     
-    img2_path = '/home/GRAMES.POLYMTL.CA/p118739/data_nvme_p118739/data/datasets/spider-challenge-2023/sub-002/anat/sub-002_acq-lowresSag_T2w.nii.gz'
+    img2_path = '/home/ge.polymtl.ca/p118739/data/datasets/spider-challenge-2023/sub-002/anat/sub-002_acq-lowresSag_T2w.nii.gz'
     img2 = Image(img2_path).change_orientation('RSP')
     img2 = resample_nib(img2, new_size=[1,1,1], new_size_type='mm', interpolation='linear')
     img2_tensor = torch.from_numpy(img2.data.copy()).to(torch.float32)
 
-    seg2_path = '/home/GRAMES.POLYMTL.CA/p118739/data_nvme_p118739/data/datasets/spider-challenge-2023/derivatives/labels/sub-002/anat/sub-002_acq-lowresSag_T2w_label-spine_dseg.nii.gz'
+    seg2_path = '/home/ge.polymtl.ca/p118739/data/datasets/spider-challenge-2023/derivatives/labels/sub-002/anat/sub-002_acq-lowresSag_T2w_label-spine_dseg.nii.gz'
     seg2 = Image(seg2_path).change_orientation('RSP')
     seg2 = resample_nib(seg2, new_size=[1,1,1], new_size_type='mm', interpolation='nn')
     seg2_tensor_all = torch.from_numpy(seg2.data.copy())