diff --git a/monai/utils/jupyter_utils.py b/monai/utils/jupyter_utils.py
index 26487083b1..ed109393f9 100644
--- a/monai/utils/jupyter_utils.py
+++ b/monai/utils/jupyter_utils.py
@@ -161,6 +161,7 @@ def plot_engine_status(
     window_fraction: int = 20,
     image_fn: Optional[Callable] = tensor_to_images,
     fig=None,
+    selected_inst: int = 0,
 ) -> Tuple:
     """
     Plot the status of the given Engine with its logger. The plot will consist of a graph of loss values and metrics
@@ -192,19 +193,33 @@ def plot_engine_status(
     imagemap = {}
     if image_fn is not None and engine.state is not None and engine.state.batch is not None:
         for src in (engine.state.batch, engine.state.output):
+            label = "Batch" if src is engine.state.batch else "Output"
+            batch_selected_inst = selected_inst  # selected batch index, set to 0 when src is decollated
+
+            # if the src object is a list of elements, ie. a decollated batch, select an element and keep it as
+            # a dictionary of tensors with a batch dimension added
             if isinstance(src, list):
-                for i, s in enumerate(src):
-                    if isinstance(s, dict):
-                        for k, v in s.items():
-                            if isinstance(v, torch.Tensor):
-                                image = image_fn(k, v)
-                                if image is not None:
-                                    imagemap[f"{k}_{i}"] = image
-                    elif isinstance(s, torch.Tensor):
-                        label = "Batch" if src is engine.state.batch else "Output"
-                        image = image_fn(label, s)
+                selected_dict = src[selected_inst]  # select this element
+                batch_selected_inst = 0  # set the selection to be the single index in the batch dimension
+                # store each tensor that is interpretable as an image with an added batch dimension
+                src = {k: v[None] for k, v in selected_dict.items() if isinstance(v, torch.Tensor) and v.ndim >= 3}
+
+            # images will be generated from the batch item selected above only, or from the single item given as `src`
+
+            if isinstance(src, dict):
+                for k, v in src.items():
+                    if isinstance(v, torch.Tensor) and v.ndim >= 4:
+                        image = image_fn(k, v[batch_selected_inst])
+
+                        # if we have images add each one separately to the map
                         if image is not None:
-                            imagemap[f"{label}_{i}"] = image
+                            for i, im in enumerate(image):
+                                imagemap[f"{k}_{i}"] = im
+
+            elif isinstance(src, torch.Tensor):
+                image = image_fn(label, src)
+                if image is not None:
+                    imagemap[f"{label}_{i}"] = image
 
     axes = plot_metric_images(fig, title, graphmap, imagemap, yscale, avg_keys, window_fraction)
 
diff --git a/tests/test_threadcontainer.py b/tests/test_threadcontainer.py
index 543dab4d0c..5613b1babd 100644
--- a/tests/test_threadcontainer.py
+++ b/tests/test_threadcontainer.py
@@ -79,7 +79,7 @@ def test_plot(self):
         # a third non-image key is added to test that this is correctly ignored when plotting
         data = {CommonKeys.IMAGE: img, CommonKeys.LABEL: img, "Not Image Data": ["This isn't an image"]}
 
-        loader = DataLoader([data] * 10)
+        loader = DataLoader([data] * 20, batch_size=2)
 
         trainer = SupervisedTrainer(
             device=torch.device("cpu"),