diff --git a/data_io/dataset_reading.py b/data_io/dataset_reading.py
index f41d9e2..49b7212 100644
--- a/data_io/dataset_reading.py
+++ b/data_io/dataset_reading.py
@@ -165,10 +165,11 @@ def get_numpy_dataset(original_dataset, input_slice, output_slice, transform):
     if output_slice is not None:
         component_erosion_steps = original_dataset.get('component_erosion_steps', 0)
         dilation_amount = 1 + component_erosion_steps
-        dilated_output_slices = tuple([slice(s.start - dilation_amount, s.stop + dilation_amount, s.step) for s in output_slice])
+        dilated_output_slices = tuple(slice(s.start - dilation_amount, s.stop + dilation_amount, s.step) for s in output_slice)
+        de_dilation_slices = (Ellipsis,) + tuple(slice(dilation_amount, -dilation_amount) for _ in output_slice)
         components, affinities, mask = get_outputs(original_dataset, dilated_output_slices)
         mask_threshold = float(original_dataset.get('mask_threshold', 0))
-        mask_fraction_of_this_batch = np.mean(mask)
+        mask_fraction_of_this_batch = np.mean(mask[de_dilation_slices])
         good_enough = mask_fraction_of_this_batch > mask_threshold
         if not good_enough:
             return None
@@ -186,7 +187,6 @@ def get_numpy_dataset(original_dataset, input_slice, output_slice, transform):
             affinities = augmented_dilated_dataset["label"]
             mask = augmented_dilated_dataset["mask"]
             image = augmented_dilated_dataset["data"]
-        de_dilation_slices = (Ellipsis,) + tuple([slice(dilation_amount, -dilation_amount) for _ in output_slice])
         dataset_numpy['components'] = components[de_dilation_slices]
         dataset_numpy['label'] = affinities[de_dilation_slices]
         dataset_numpy['mask'] = mask[de_dilation_slices]