diff --git a/acceleration/distributed_training/unet_evaluation_workflows.py b/acceleration/distributed_training/unet_evaluation_workflows.py
index be817fd245..847fb12e5b 100644
--- a/acceleration/distributed_training/unet_evaluation_workflows.py
+++ b/acceleration/distributed_training/unet_evaluation_workflows.py
@@ -31,7 +31,7 @@
 - Wrap the model with `DistributedDataParallel` after moving to expected device.
 - Put model file on every node, then load and map to expected GPU device in every process.
 - Wrap Dataset with `DistributedSampler`, disable the `shuffle` in sampler and DataLoader.
-- Add `StatsHandler` and `SegmentationSaver` to the master process which is `dist.get_rank() == 0`.
+- Add `StatsHandler` to the master process which is `dist.get_rank() == 0`.
 - ignite can automatically reduce metrics for distributed evaluation, refer to:
   https://github.com/pytorch/ignite/blob/v0.4.2/ignite/metrics/metric.py#L507
 
diff --git a/modules/dynunet_pipeline/evaluator.py b/modules/dynunet_pipeline/evaluator.py
index aa198a5f64..184e422f63 100644
--- a/modules/dynunet_pipeline/evaluator.py
+++ b/modules/dynunet_pipeline/evaluator.py
@@ -42,7 +42,7 @@ class DynUNetEvaluator(SupervisedEvaluator):
             checkpoint into files.
         additional_metrics: more Ignite metrics that also attach to Ignite Engine.
         val_handlers: every handler is a set of Ignite Event-Handlers, must have `attach` function, like:
-            CheckpointHandler, StatsHandler, SegmentationSaver, etc.
+            CheckpointHandler, StatsHandler, etc.
         amp: whether to enable auto-mixed-precision evaluation, default is False.
         tta_val: whether to do the 8 flips (8 = 2 ** 3, where 3 represents the three dimensions)
             test time augmentation, default is False.