diff --git a/HypVINN/run_prediction.py b/HypVINN/run_prediction.py
index f64b117b..5a275cfe 100644
--- a/HypVINN/run_prediction.py
+++ b/HypVINN/run_prediction.py
@@ -32,7 +32,7 @@
 )
 from FastSurferCNN.utils.common import assert_no_root, SerialExecutor
 
-from HypVINN.config.hypvinn_files import HYPVINN_SEG_NAME
+from HypVINN.config.hypvinn_files import HYPVINN_SEG_NAME, HYPVINN_MASK_NAME
 from HypVINN.data_loader.data_utils import hypo_map_label2subseg, rescale_image
 from HypVINN.inference import Inference
 from HypVINN.utils import ModalityDict, ModalityMode, ViewOperations
@@ -173,6 +173,7 @@ def main(
         cfg_cor: Path,
         cfg_sag: Path,
         hypo_segfile: str = HYPVINN_SEG_NAME,
+        hypo_maskfile: str = HYPVINN_MASK_NAME,
         allow_root: bool = False,
         qc_snapshots: bool = False,
         reg_mode: Literal["coreg", "robust", "none"] = "coreg",
@@ -209,6 +210,8 @@ def main(
         The path to the sagittal configuration file.
     hypo_segfile : str, default="{HYPVINN_SEG_NAME}"
         The name of the hypothalamus segmentation file. Default is {HYPVINN_SEG_NAME}.
+    hypo_maskfile : str, default="{HYPVINN_MASK_NAME}"
+        The name of the hypothalamus mask file. Default is {HYPVINN_MASK_NAME}.
     allow_root : bool, default=False
         Whether to allow running as root user. Default is False.
     qc_snapshots : bool, optional
@@ -224,7 +227,8 @@ def main(
     device : str, default="auto"
         The device to use. Default is "auto", which automatically selects the device.
     viewagg_device : str, default="auto"
-        The view aggregation device to use. Default is "auto", which automatically selects the device.
+        The view aggregation device to use. Default is "auto", which automatically 
+        selects the device.
 
     Returns
     -------
@@ -360,21 +364,17 @@ def main(
         else:
             orig_path = t2_path
 
-        save_future: Future = pool.submit(
-            save_segmentation,
+        time_needed = save_segmentation(
             pred_classes,
             orig_path=orig_path,
             ras_affine=affine,
             ras_header=header,
             subject_dir=subject_dir,
             seg_file=hypo_segfile,
+            mask_file=hypo_maskfile,
             save_mask=True,
         )
-        save_future.add_done_callback(
-            lambda x: logger.info(
-                f"Prediction successfully saved in {x.result()} seconds."
-            ),
-        )
+        logger.info(f"Prediction successfully saved in {time_needed} seconds.")
         if qc_snapshots:
             qc_future: Optional[Future] = pool.submit(
                 plot_qc_images,
@@ -408,7 +408,6 @@ def main(
         if qc_future:
             # finish qc
             qc_future.result()
-        save_future.result()
 
         logger.info(
             f"Processing whole pipeline finished in {time() - start:.4f} seconds."
diff --git a/HypVINN/utils/img_processing_utils.py b/HypVINN/utils/img_processing_utils.py
index ea0d902c..765f8baf 100644
--- a/HypVINN/utils/img_processing_utils.py
+++ b/HypVINN/utils/img_processing_utils.py
@@ -51,7 +51,8 @@ def save_segmentation(
         ras_affine: npt.NDArray[float],
         ras_header: nib.nifti1.Nifti1Header | nib.nifti2.Nifti2Header | nib.freesurfer.mghformat.MGHHeader,
         subject_dir: Path,
-        seg_file: Path,
+        seg_file: str,
+        mask_file: str,
         save_mask: bool = False,
 ) -> float:
     """
@@ -73,7 +74,9 @@ def save_segmentation(
     subject_dir : Path
         The directory where the subject's data is stored.
     seg_file : Path
-        The file where the segmentation results will be saved.
+        The file where the segmentation will be saved (relative to subject_dir/mri).
+    mask_file : str
+        The file where the mask will be saved (relative to subject_dir/mri).
     save_mask : bool, default=False
         Whether to save the mask or not. Default is False.
 
@@ -86,7 +89,6 @@ def save_segmentation(
     from time import time
     starttime = time()
     from HypVINN.data_loader.data_utils import reorient_img
-    from HypVINN.config.hypvinn_files import HYPVINN_MASK_NAME, HYPVINN_SEG_NAME
 
     pred_arr, labels_cc = get_clean_labels(np.array(prediction, dtype=np.uint8))
     # Mapped HypVINN labelst to FreeSurfer Hypvinn Labels
@@ -101,7 +103,7 @@ def save_segmentation(
         LOGGER.info(
             f"HypoVINN Mask after re-orientation: {img2axcodes(mask_img)}"
         )
-        nib.save(mask_img, subject_dir / "mri" / HYPVINN_MASK_NAME)
+        nib.save(mask_img, subject_dir / "mri" / mask_file)
 
     pred_img = nib.Nifti1Image(pred_arr, affine=ras_affine, header=ras_header)
     LOGGER.info(f"HypoVINN Prediction orientation: {img2axcodes(pred_img)}")
@@ -110,7 +112,7 @@ def save_segmentation(
         f"HypoVINN Prediction after re-orientation: {img2axcodes(pred_img)}"
     )
     pred_img.set_data_dtype(np.int16)  # Maximum value 939
-    nib.save(pred_img, subject_dir / seg_file)
+    nib.save(pred_img, subject_dir / "mri" / seg_file)
     return time() - starttime