diff --git a/nnunetv2/training/nnUNetTrainer/variants/data_augmentation/nnUNetTrainerDAOrd0.py b/nnunetv2/training/nnUNetTrainer/variants/data_augmentation/nnUNetTrainerDAOrd0.py
index e87ff8f92..be31857b3 100644
--- a/nnunetv2/training/nnUNetTrainer/variants/data_augmentation/nnUNetTrainerDAOrd0.py
+++ b/nnunetv2/training/nnUNetTrainer/variants/data_augmentation/nnUNetTrainerDAOrd0.py
@@ -102,3 +102,56 @@ def get_dataloaders(self):
                                            max(1, allowed_num_processes // 2), 3, None, True, 0.02)
 
         return mt_gen_train, mt_gen_val
+
+
+class nnUNetTrainer_DASegOrd0_NoMirroring(nnUNetTrainer):
+    def get_dataloaders(self):
+        """
+        changed order_resampling_data, order_resampling_seg
+        """
+        # we use the patch size to determine whether we need 2D or 3D dataloaders. We also use it to determine whether
+        # we need to use dummy 2D augmentation (in case of 3D training) and what our initial patch size should be
+        patch_size = self.configuration_manager.patch_size
+        dim = len(patch_size)
+
+        # needed for deep supervision: how much do we need to downscale the segmentation targets for the different
+        # outputs?
+        deep_supervision_scales = self._get_deep_supervision_scales()
+
+        rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes = \
+            self.configure_rotation_dummyDA_mirroring_and_inital_patch_size()
+
+        # Deactivate mirroring data augmentation
+        mirror_axes = None
+        self.inference_allowed_mirroring_axes = None
+
+        # training pipeline
+        tr_transforms = self.get_training_transforms(
+            patch_size, rotation_for_DA, deep_supervision_scales, mirror_axes, do_dummy_2d_data_aug,
+            order_resampling_data=3, order_resampling_seg=0,
+            use_mask_for_norm=self.configuration_manager.use_mask_for_norm,
+            is_cascaded=self.is_cascaded, foreground_labels=self.label_manager.all_labels,
+            regions=self.label_manager.foreground_regions if self.label_manager.has_regions else None,
+            ignore_label=self.label_manager.ignore_label)
+
+        # validation pipeline
+        val_transforms = self.get_validation_transforms(deep_supervision_scales,
+                                                        is_cascaded=self.is_cascaded,
+                                                        foreground_labels=self.label_manager.all_labels,
+                                                        regions=self.label_manager.foreground_regions if
+                                                        self.label_manager.has_regions else None,
+                                                        ignore_label=self.label_manager.ignore_label)
+
+        dl_tr, dl_val = self.get_plain_dataloaders(initial_patch_size, dim)
+
+        allowed_num_processes = get_allowed_n_proc_DA()
+        if allowed_num_processes == 0:
+            mt_gen_train = SingleThreadedAugmenter(dl_tr, tr_transforms)
+            mt_gen_val = SingleThreadedAugmenter(dl_val, val_transforms)
+        else:
+            mt_gen_train = LimitedLenWrapper(self.num_iterations_per_epoch, dl_tr, tr_transforms,
+                                             allowed_num_processes, 6, None, True, 0.02)
+            mt_gen_val = LimitedLenWrapper(self.num_val_iterations_per_epoch, dl_val, val_transforms,
+                                           max(1, allowed_num_processes // 2), 3, None, True, 0.02)
+
+        return mt_gen_train, mt_gen_val