diff --git a/3d_segmentation/challenge_baseline/README.md b/3d_segmentation/challenge_baseline/README.md
index fbef7c4a6b..3822197724 100644
--- a/3d_segmentation/challenge_baseline/README.md
+++ b/3d_segmentation/challenge_baseline/README.md
@@ -64,7 +64,7 @@ During training, the top three models will be selected based on the per-epoch va
 
 The training uses convenient file loading modules and a few intensity and spatial random augmentations using [MONAI](https://github.com/Project-MONAI/MONAI):
 
-- `LoadImaged`, `AddChanneld`, `Orientationd`, `Spacingd`, `ScaleIntensityRanged`
+- `LoadImaged`, `Orientationd`, `Spacingd`, `ScaleIntensityRanged`
 
 Load the image data into the LPS orientation (Left to right, Posterior to anterior, Superior to inferior), with a resolution of 1.25mm x 1.25mm x 5.00mm, and intensity between [-1000.0, 500.0] scaled to [0.0, 1.0].
 
diff --git a/3d_segmentation/swin_unetr_brats21_segmentation_3d.ipynb b/3d_segmentation/swin_unetr_brats21_segmentation_3d.ipynb
index c3963ef59c..ae2bb39fb0 100644
--- a/3d_segmentation/swin_unetr_brats21_segmentation_3d.ipynb
+++ b/3d_segmentation/swin_unetr_brats21_segmentation_3d.ipynb
@@ -479,7 +479,7 @@
     "torch.backends.cudnn.benchmark = True\n",
     "dice_loss = DiceLoss(to_onehot_y=False, sigmoid=True)\n",
     "post_sigmoid = Activations(sigmoid=True)\n",
-    "post_pred = AsDiscrete(argmax=False, logit_thresh=0.5)\n",
+    "post_pred = AsDiscrete(argmax=False, threshold=0.5)\n",
     "dice_acc = DiceMetric(\n",
     "    include_background=True, reduction=MetricReduction.MEAN_BATCH, get_not_nans=True\n",
     ")\n",