diff --git a/n2v/internals/N2V_DataGenerator.py b/n2v/internals/N2V_DataGenerator.py
index 588ae32..33ca496 100644
--- a/n2v/internals/N2V_DataGenerator.py
+++ b/n2v/internals/N2V_DataGenerator.py
@@ -131,8 +131,9 @@ def generate_patches_from_list(self, data, num_patches_per_img=None, shape=(256,
         """
         patches = []
         for img in data:
-            p = self.generate_patches(img, num_patches=num_patches_per_img, shape=shape, augment=augment)
-            patches.append(p)
+            for s in range(img.shape[0]):
+                p = self.generate_patches(img[s][np.newaxis], num_patches=num_patches_per_img, shape=shape, augment=augment)
+                patches.append(p)
 
         patches = np.concatenate(patches, axis=0)
 
@@ -210,12 +211,11 @@ def __extract_patches__(self, data, num_patches=None, shape=(256, 256), n_dims=2
             patches = []
             if n_dims == 2:
                 for i in range(num_patches):
-                    s = np.random.randint(0, data.shape[0])
                     y, x = np.random.randint(0, data.shape[1] - shape[0] + 1), np.random.randint(0,
                                                                                                  data.shape[
                                                                                                           2] - shape[
                                                                                                           1] + 1)
-                    patches.append(data[s, y:y + shape[0], x:x + shape[1]])
+                    patches.append(data[0, y:y + shape[0], x:x + shape[1]])
 
                 if len(patches) > 1:
                     return np.stack(patches)
@@ -223,13 +223,12 @@ def __extract_patches__(self, data, num_patches=None, shape=(256, 256), n_dims=2
                     return np.array(patches)[np.newaxis]
             elif n_dims == 3:
                 for i in range(num_patches):
-                    s = np.random.randint(0, data.shape[0])
                     z, y, x = np.random.randint(0, data.shape[1] - shape[0] + 1), np.random.randint(0,
                                                                                                     data.shape[
                                                                                                              2] - shape[
                                                                                                              1] + 1), np.random.randint(
                         0, data.shape[3] - shape[2] + 1)
-                    patches.append(data[s, z:z + shape[0], y:y + shape[1], x:x + shape[2]])
+                    patches.append(data[0, z:z + shape[0], y:y + shape[1], x:x + shape[2]])
 
                 if len(patches) > 1:
                     return np.stack(patches)