diff --git a/src/spikeinterface/core/analyzer_extension_core.py b/src/spikeinterface/core/analyzer_extension_core.py
index fea3f3618e..42b25f1a5d 100644
--- a/src/spikeinterface/core/analyzer_extension_core.py
+++ b/src/spikeinterface/core/analyzer_extension_core.py
@@ -437,6 +437,7 @@ def _run(self, verbose=False, **job_kwargs):
             return_in_uV = self.sorting_analyzer.return_in_uV
 
             return_std = "std" in self.params["operators"]
+            sparsity_mask = None if self.sparsity is None else self.sparsity.mask
             output = estimate_templates_with_accumulator(
                 recording,
                 some_spikes,
@@ -445,17 +446,30 @@ def _run(self, verbose=False, **job_kwargs):
                 self.nafter,
                 return_in_uV=return_in_uV,
                 return_std=return_std,
+                sparsity_mask=sparsity_mask,
                 verbose=verbose,
                 **job_kwargs,
             )
 
-            # Output of estimate_templates_with_accumulator is either (templates,) or (templates, stds)
             if return_std:
                 templates, stds = output
-                self.data["average"] = templates
-                self.data["std"] = stds
+                data = dict(average=templates, std=stds)
             else:
-                self.data["average"] = output
+                templates = output
+                data = dict(average=templates)
+
+            if self.sparsity is not None:
+                # make average and std dense again
+                for k, arr in data.items():
+                    dense_arr = np.zeros(
+                        (arr.shape[0], arr.shape[1], self.sorting_analyzer.get_num_channels()),
+                        dtype=arr.dtype,
+                    )
+                    for unit_index, unit_id in enumerate(self.sorting_analyzer.unit_ids):
+                        chan_inds = self.sparsity.unit_id_to_channel_indices[unit_id]
+                        dense_arr[unit_index][:, chan_inds] = arr[unit_index, :, : chan_inds.size]
+                    data[k] = dense_arr
+            self.data.update(data)
 
     def _compute_and_append_from_waveforms(self, operators):
         if not self.sorting_analyzer.has_extension("waveforms"):