diff --git a/NiChart_DLMUSE/__init__.py b/NiChart_DLMUSE/__init__.py
index 7f41d576..7612d577 100644
--- a/NiChart_DLMUSE/__init__.py
+++ b/NiChart_DLMUSE/__init__.py
@@ -1,4 +1 @@
-from .dlmuse_pipeline import run_pipeline, run_dlicv, run_dlmuse
-
-if __name__ == "__main__":
-    pass
+from .dlmuse_pipeline import run_dlicv, run_dlmuse, run_pipeline
diff --git a/NiChart_DLMUSE/__main__.py b/NiChart_DLMUSE/__main__.py
index 026353a0..f879750e 100644
--- a/NiChart_DLMUSE/__main__.py
+++ b/NiChart_DLMUSE/__main__.py
@@ -87,7 +87,7 @@ def main() -> None:
         "--cores",
         type=str,
         help="Number of cores",
-        default=4,
+        default=1,
         required=False,
     )
 
@@ -131,6 +131,14 @@ def main() -> None:
         help="Pass additional args to be sent to DLICV (ex. '-nps 1 -npp 1'). It is recommended to surround these args in a set of double quotes. See the DLICV documentation for details.",
     )
 
+    parser.add_argument(
+        "--refaced-data",
+        action="store_true",
+        required=False,
+        default=False,
+        help="If set, refine DLICV mask by keeping only the largest connected component (for refaced data).",
+    )
+
     # HELP argument
     help = "Show this message and exit"
     parser.add_argument("-h", "--help", action="store_true", help=help)
@@ -142,6 +150,7 @@ def main() -> None:
     device = args.device
     dlicv_extra_args = args.dlicv_args
     dlmuse_extra_args = args.dlmuse_args
+    refaced_data = args.refaced_data
 
     print()
     print("Arguments:")
@@ -185,6 +194,7 @@ def main() -> None:
                         device,
                         dlmuse_extra_args,
                         dlicv_extra_args,
+                        refaced_data,
                         i,
                     ),
                 )
@@ -198,7 +208,14 @@ def main() -> None:
             remove_subfolders("raw_temp_T1")
             remove_subfolders(out_dir)
         else:  # No core parallelization
-            run_pipeline(in_dir, out_dir, device, dlmuse_extra_args, dlicv_extra_args)
+            run_pipeline(
+                in_dir,
+                out_dir,
+                device,
+                dlmuse_extra_args,
+                dlicv_extra_args,
+                refaced_data,
+            )
 
     else:  # Non-BIDS
         if int(args.cores) > 1:
@@ -216,6 +233,7 @@ def main() -> None:
                         device,
                         dlmuse_extra_args,
                         dlicv_extra_args,
+                        refaced_data,
                         i,
                     ),
                 )
@@ -229,7 +247,14 @@ def main() -> None:
             remove_subfolders(in_dir)
             remove_subfolders(out_dir)
         else:  # No core parallelization
-            run_pipeline(in_dir, out_dir, device, dlmuse_extra_args, dlicv_extra_args)
+            run_pipeline(
+                in_dir,
+                out_dir,
+                device,
+                dlmuse_extra_args,
+                dlicv_extra_args,
+                refaced_data,
+            )
 
 
 if __name__ == "__main__":
diff --git a/NiChart_DLMUSE/dlmuse_pipeline.py b/NiChart_DLMUSE/dlmuse_pipeline.py
index 8268fd79..c6642d89 100644
--- a/NiChart_DLMUSE/dlmuse_pipeline.py
+++ b/NiChart_DLMUSE/dlmuse_pipeline.py
@@ -1,5 +1,6 @@
 import logging
 import os
+import sys
 
 import pkg_resources  # type: ignore
 
@@ -32,7 +33,9 @@
 )
 
 logger = logging.getLogger(__name__)
-logging.basicConfig(filename="pipeline.log", encoding="utf-8", level=logging.DEBUG)
+logging.basicConfig(encoding="utf-8", level=logging.DEBUG,
+                    handlers=[logging.FileHandler("pipeline.log"),
+                              logging.StreamHandler(sys.stdout)])
 
 
 def run_pipeline(
@@ -41,6 +44,7 @@ def run_pipeline(
     device: str,
     dlmuse_extra_args: str = '',
     dlicv_extra_args: str = '',
+    refaced_data: bool = False,
     sub_fldr: int = 1,
     progress_bar = None,
 ) -> None:
@@ -108,6 +112,24 @@ def run_pipeline(
         progress_bar.set_description("Running DLICV")
     run_dlicv(in_dir, in_suff, out_dir, out_suff, device, dlicv_extra_args)
 
+    # If refaced data is specified, refine the masks used in the next step (s3_masked)
+    if refaced_data:
+        import SimpleITK as sitk
+
+        for _, tmp_row in df_img.iterrows():
+            img_prefix = tmp_row.img_prefix
+            fpath = os.path.join(out_dir, img_prefix + SUFF_DLICV)
+            if os.path.exists(fpath):
+                s2_dlicv_output = sitk.ReadImage(fpath)
+                # Keep only the largest connected component
+                mask_component = sitk.ConnectedComponent(s2_dlicv_output)
+                mask_sorted_component = sitk.RelabelComponent(
+                    mask_component, sortByObjectSize=True
+                )
+                final_mask = sitk.Equal(mask_sorted_component, 1)
+                # Write refined mask back in-place within s2_dlicv
+                sitk.WriteImage(final_mask, fpath)
+
     logging.info(f"Applying DLICV for batch [{sub_fldr}] done")
 
     logging.info(f"Applying mask for batch [{sub_fldr}]...")
diff --git a/NiChart_DLMUSE/utils.py b/NiChart_DLMUSE/utils.py
index f4c4e310..60966f66 100644
--- a/NiChart_DLMUSE/utils.py
+++ b/NiChart_DLMUSE/utils.py
@@ -65,7 +65,9 @@ def remove_common_suffix(list_files: list) -> list:
 
     bnames = list_files
     if len(list_files) == 1:
-        if list_files[0].endswith('_T1'): # If there is a single image with suffix _T1, remove it
+        if list_files[0].endswith(
+            "_T1"
+        ):  # If there is a single image with suffix _T1, remove it
             bnames = [x[0:-3] for x in bnames]
         return bnames
 
diff --git a/README.rst b/README.rst
index d7b23f9f..b458232f 100644
--- a/README.rst
+++ b/README.rst
@@ -53,11 +53,11 @@ Or from out latest stable PyPI wheel: ::
 (If needed for your system) Install PyTorch with compatible CUDA.
 You only need to run this step if you experience errors with CUDA while running NiChart_DLMUSE.
 Run "pip uninstall torch torchaudio torchvision".
-Then follow the `PyTorch installation instructions <https://pytorch.org/get-started/locally/>`_ for your CUDA version.
-Note that we highly recommend matching the torch version you install to the version used by NiChart_DLMUSE. For example, after installing NiChart_DLMUSE: ::
+Then follow the `PyTorch installation instructions <https://pytorch.org/get-started/locally/>`_ for your CUDA version. 
+Specific versions are needed for full compatibility. On Linux, download Torch 2.3.1, on Windows install 2.5.1. For example, after installing NiChart_DLMUSE: ::
 
    $ pip uninstall torch torchvision torchaudio
-     pip install torch==2.2.1 --index-url https://download.pytorch.org/whl/cu121
+     pip install torch==2.3.1 --index-url https://download.pytorch.org/whl/cu121
 
 ******************
 Run NiChart_DLMUSE
@@ -77,7 +77,7 @@ Docker build
 ************
 
 The package comes already pre-built as a `docker container <https://hub.docker.com/repository/docker/cbica/nichart_dlmuse/general>`_, for convenience. Please see `Usage <#usage>`_ for more information on how to use it. Alternatively, you can build the docker image
-locally, like so: ::
+locally from the source repo, like so: ::
 
   $ docker build -t cbica/nichart_dlmuse .
 
diff --git a/docs/installation.rst b/docs/installation.rst
index 82b9555b..da6f4a1e 100644
--- a/docs/installation.rst
+++ b/docs/installation.rst
@@ -30,7 +30,9 @@ You can build the package by running the following command: ::
 
     $ docker build -t cbica/nichart_dlmuse .
 
-.. _`Singularity/Apptainer build`
+***************************
+Singularity/Apptainer build
+***************************
 
 Singularity and Apptainer images can be built for NiChart_DLMUSE, allowing for frozen versions of the pipeline and easier
 installation for end-users. Note that the Singularity project recently underwent a rename to "Apptainer", with a commercial
@@ -43,7 +45,9 @@ After installing the container engine, run: ::
 This will take some time, but will build a containerized version of your current repo. Be aware that this includes any local changes!
 The nichart_dlmuse.sif file can be distributed via direct download, or pushed to a container registry that accepts SIF images.
 
-.. _`Manual installation`
+*******************
+Manual installation
+*******************
 
 You can manually build the package from source by running: ::
 
@@ -51,5 +55,6 @@ You can manually build the package from source by running: ::
 
     $ cd NiChart_DLMUSE && python3 -m pip install -e .
 
-We **do not** recomment installing the package directly from source as the repository above is under heavy development and can cause
-crashes and bugs.
+.. note::
+    We **do not** recommend installing the package directly from source as the repository above is under heavy development and can cause
+    crashes and bugs.
diff --git a/requirements.txt b/requirements.txt
index 79414b1a..2428040f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,6 +4,7 @@ DLICV
 DLMUSE
 nibabel>=5.2
 scipy
+SimpleITK
 
 # Developer tools
 pytest
diff --git a/scripts/wrapper.py b/scripts/wrapper.py
new file mode 100644
index 00000000..254175bc
--- /dev/null
+++ b/scripts/wrapper.py
@@ -0,0 +1,59 @@
+import argparse
+import os
+import shutil
+import tempfile
+from pathlib import Path
+
+# This wrapper script just adapts NiChart_DLMUSE to take two separate output args. Everything else is passed transparently
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Wrapper", allow_abbrev=False)
+    parser.add_argument("-i", "--in_dir", required=True, help="Input directory")
+    parser.add_argument(
+        "-o1",
+        "--out_segs",
+        required=True,
+        help="Output directory for segmentation files",
+    )
+    parser.add_argument(
+        "-o2", "--out_csvs", required=True, help="Output directory for CSV files"
+    )
+
+    # Parse known args; leave the rest for original app
+    args, extra_args = parser.parse_known_args()
+
+    input_dir = args.in_dir
+    seg_dir = Path(args.out_segs)
+    csv_dir = Path(args.out_csvs)
+
+    seg_dir.mkdir(parents=True, exist_ok=True)
+    csv_dir.mkdir(parents=True, exist_ok=True)
+
+    with tempfile.TemporaryDirectory() as tmp_output:
+        tmp_output_path = Path(tmp_output)
+
+        # Build command to run original application
+        cmd = [
+            "NiChart_DLMUSE",
+            "-i",
+            input_dir,
+            "-o",
+            str(tmp_output_path),
+        ] + extra_args
+        command = " ".join(cmd)
+        os.system(command)
+
+        # Copy output files
+        for item in tmp_output_path.rglob("*"):
+            if item.is_file():
+                if item.suffix.lower() == ".csv":
+                    shutil.copy2(item, csv_dir / item.name)
+                else:
+                    dest_path = seg_dir / item.relative_to(tmp_output_path)
+                    dest_path.parent.mkdir(parents=True, exist_ok=True)
+                    shutil.copy2(item, dest_path)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/setup.py b/setup.py
index b353a48a..9698ec15 100644
--- a/setup.py
+++ b/setup.py
@@ -7,7 +7,7 @@
 
 setup(
     name="NiChart_DLMUSE",
-    version="1.0.8",
+    version="1.0.9",
     description="Run NiChart_DLMUSE on your data (currently only structural pipeline is supported).",
     long_description=long_description,
     long_description_content_type="text/markdown",