diff --git a/src/roiextractors/segmentationextractor.py b/src/roiextractors/segmentationextractor.py
index 61229ba0..998d7cde 100644
--- a/src/roiextractors/segmentationextractor.py
+++ b/src/roiextractors/segmentationextractor.py
@@ -139,10 +139,10 @@ def get_roi_pixel_masks(self, roi_ids=None) -> np.array:
         -------
         pixel_masks: list
             List of length number of rois, each element is a 2-D array with shape (number_of_non_zero_pixels, 3).
-            Columns 1 and 2 are the x and y coordinates of the pixel, while the third column represents the weight of
+            Columns 1 and 2 are the row and column coordinates of the pixel, while the third column represents the weight of
             the pixel.
         """
-        return _pixel_mask_extractor(image_masks=self.get_roi_image_masks(roi_ids=roi_ids))
+        return convert_image_masks_to_pixel_masks(image_masks=self.get_roi_image_masks(roi_ids=roi_ids))
 
     @abstractmethod
     def get_roi_response_traces(
@@ -225,7 +225,7 @@ def get_background_pixel_masks(self, background_ids=None) -> np.array:
             Columns 1 and 2 are the x and y coordinates of the pixel, while the third column represents the weight of
             the pixel.
         """
-        return _pixel_mask_extractor(self.get_background_image_masks(background_ids=background_ids))
+        return convert_image_masks_to_pixel_masks(self.get_background_image_masks(background_ids=background_ids))
 
     @abstractmethod
     def get_background_response_traces(
@@ -411,7 +411,7 @@ def get_summary_images(self, names: Optional[list[str]] = None) -> dict:
         return self._parent_segmentation.get_summary_images(names=names)
 
 
-def _pixel_mask_extractor(image_masks: np.ndarray) -> list:
+def convert_image_masks_to_pixel_masks(image_masks: np.ndarray) -> list:
     """Convert image masks to pixel masks.
 
     Pixel masks are an alternative data format for storage of image masks which relies on the sparsity of the images.
@@ -426,7 +426,7 @@ def _pixel_mask_extractor(image_masks: np.ndarray) -> list:
     -------
     pixel_masks: list
         List of length number of rois, each element is a 2-D array with shape (number_of_non_zero_pixels, 3).
-        Columns 1 and 2 are the x and y coordinates of the pixel, while the third column represents the weight of
+        Columns 1 and 2 are the row and column coordinates of the pixel, while the third column represents the weight of
         the pixel.
     """
     pixel_mask_list = []
@@ -438,28 +438,27 @@ def _pixel_mask_extractor(image_masks: np.ndarray) -> list:
     return pixel_mask_list
 
 
-def _image_mask_extractor(pixel_mask, _roi_ids, image_shape) -> np.ndarray:
-    """Convert a pixel mask to image mask.
+def convert_pixel_masks_to_image_masks(pixel_masks: list[np.ndarray], image_shape: tuple) -> np.ndarray:
+    """Convert pixel masks to image masks.
 
     Parameters
     ----------
-    pixel_mask: list
-        list of pixel masks (no pixels X 3)
-    _roi_ids: list
-        list of roi ids with length number_of_rois
-    image_shape: array_like
-        shape of the image (number_of_rows, number_of_columns)
+    pixel_masks: list[np.ndarray]
+        List of pixel mask arrays (number_of_non_zero_pixels X 3) for each ROI.
+    image_shape: tuple
+        Shape of the image (number_of_rows, number_of_columns).
 
     Returns
     -------
-    image_mask: np.ndarray
+    image_masks: np.ndarray
         Dense representation of the ROIs with shape (number_of_rows, number_of_columns, number_of_rois).
     """
-    image_mask = np.zeros(list(image_shape) + [len(_roi_ids)])
-    for no, rois in enumerate(_roi_ids):
-        for y, x, wt in pixel_mask[rois]:
-            image_mask[int(y), int(x), no] = wt
-    return image_mask
+    shape = (*image_shape, len(pixel_masks))
+    image_masks = np.zeros(shape=shape)
+    for i, pixel_mask in enumerate(pixel_masks):
+        for row, column, wt in pixel_mask:
+            image_masks[row, column, i] = wt
+    return image_masks
 
 
 def get_default_roi_locations_from_image_masks(image_masks: np.ndarray) -> np.ndarray: