diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 811c723..b1d09ad 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -43,7 +43,7 @@ jobs:
           cd ~/data
           rm -rf large
           mkdir large
-          docker run -v "$PWD":/data s3seg:test-large /bin/bash -c "cd /data; \
+          docker run -v "$PWD":/data -u root s3seg:test-large /bin/bash -c "cd /data; \
             python /app/S3segmenter.py --imagePath $IMG --stackProbPath $PMAP --outputPath large"
 
       # If the action is successful, the output will be available as a downloadable artifact
diff --git a/large/.dev/create-docker-env.md b/large/.dev/create-docker-env.md
new file mode 100644
index 0000000..75353be
--- /dev/null
+++ b/large/.dev/create-docker-env.md
@@ -0,0 +1,40 @@
+# Note for creating/building docker image for palom ([reference](https://micromamba-docker.readthedocs.io/en/latest/advanced_usage.html#advanced-usages))
+
+1. Create reference env on micromamba's docker image
+
+    ```bash
+    # Run bash in micromamba docker image with bind volume for writing out env
+    # lock file
+    docker run -it --rm --platform linux/amd64 -v "$(pwd)":/data mambaorg/micromamba:1.4.9 bash
+    ```
+
+    ```bash
+    # Manually install known deps in `palom` env 
+    micromamba create -y -n palom python=3.10 "scikit-image<0.20" scikit-learn "zarr<2.15" tifffile imagecodecs matplotlib tqdm scipy dask numpy loguru=0.5.3 "ome-types>0.3" "pydantic<2" pint napari-lazy-openslide yamale fire termcolor dask-image -c conda-forge
+
+
+    # Use `pip install --dry-run` to verify, would only expect to see `opencv`,
+    # and `palom`
+    micromamba activate palom
+    python -m pip install --dry-run palom
+    # output: Would install opencv-python-4.8.0.76 palom-2023.8.1
+
+
+    # if the above checks out, export micromamba env as lock file
+    micromamba env export --explicit > /data/docker-env.lock
+
+
+    # pip install the rest of the packages, note: use `opencv-python-headless`
+    # instead of `opencv-python`
+    python -m pip install --no-deps palom==2023.8.1 opencv-python-headless==4.8.0.76
+
+
+    # Test the environment
+    python -c "import cv2; cv2.blur"
+    ```
+
+1. When building the docker image, specify `--platform linux/amd64`
+
+    ```bash
+    docker build --platform linux/amd64 --tag test-s3seg-large .
+    ```
\ No newline at end of file
diff --git a/large/Dockerfile b/large/Dockerfile
index f12e3e2..1cafd80 100644
--- a/large/Dockerfile
+++ b/large/Dockerfile
@@ -1,17 +1,18 @@
-FROM python:3.10
+FROM mambaorg/micromamba:1.4.9
 
-RUN python -m pip install \
-    palom>=2022.9.1 \ 
-    dask[dataframe] \
-    dask-image \
-    ome_types
-RUN python -m pip uninstall opencv-python -y
-RUN python -m pip install opencv-python-headless
+COPY --chown=$MAMBA_USER:$MAMBA_USER docker-env.lock /tmp/docker-env.lock
+RUN micromamba install --name base --yes --file /tmp/docker-env.lock \
+    && micromamba clean --trash -aflp --yes
 
-COPY S3segmenter.py ./app/S3segmenter.py
-COPY save_tifffile_pyramid.py ./app/save_tifffile_pyramid.py
-COPY watershed.py ./app/watershed.py
-COPY s3seg_qc.py ./app/s3seg_qc.py
-COPY s3seg_util.py ./app/s3seg_util.py
-COPY ignored_args.py ./app/ignored_args.py
+# pip install packages that are not available/problematic on conda-forge
+RUN /opt/conda/bin/python -m pip install \
+    --no-deps \
+    opencv-python-headless==4.8.0.76 \
+    palom==2023.8.1 \
+    && /opt/conda/bin/python -m pip cache purge
 
+# copy scripts to /app/
+COPY / /app/
+
+# add conda path to PATH to allow entrypoint overwrite
+ENV PATH="${PATH}:/opt/conda/bin"
\ No newline at end of file
diff --git a/large/S3segmenter.py b/large/S3segmenter.py
index 1141b96..0a96312 100644
--- a/large/S3segmenter.py
+++ b/large/S3segmenter.py
@@ -3,12 +3,13 @@
 import pathlib 
  
 import watershed 
-import s3seg_util
 import s3seg_qc
 import ignored_args
 
 import logging
- 
+
+import palom.reader
+
 def main(argv=sys.argv): 
  
     parser = argparse.ArgumentParser( 
@@ -67,13 +68,14 @@ def main(argv=sys.argv):
         pixel_size = args.pixelSize
         logging.info(f"Pixel size: {pixel_size} (user supplied)")
     else:
-        pixel_size = s3seg_util.detect_pixel_size(img_path)
-        if pixel_size is None:
+        try:
+            pixel_size = palom.reader.OmePyramidReader(img_path).pixel_size
+        except Exception as err:
+            print(err)
             logging.error(
                 'Auto-detect pixel size failed, use `--pixelSize SIZE` to specify it'
             )
             return 1
-        logging.info(f"Pixel size: {pixel_size} (from ome-xml)")
 
     watershed.main([ 
         '', 
@@ -89,7 +91,7 @@ def main(argv=sys.argv):
 
     s3seg_qc.run_mcmicro(
         out_path,
-        qc_dir / 'nucleiRingOutlines.ome.tif',
+        qc_dir / f"{img_stem}-nucleiRingOutlines.ome.tif",
         pmap_path=args.stackProbPath,
         img_path=args.imagePath,
         img_channels=img_channels,
diff --git a/large/_version.py b/large/_version.py
new file mode 100644
index 0000000..2e6e772
--- /dev/null
+++ b/large/_version.py
@@ -0,0 +1 @@
+VERSION = '1.5.5'
\ No newline at end of file
diff --git a/large/docker-env.lock b/large/docker-env.lock
new file mode 100644
index 0000000..d244b67
--- /dev/null
+++ b/large/docker-env.lock
@@ -0,0 +1,272 @@
+# This file may be used to create an environment using:
+# $ conda create --name <env> --file <this file>
+# platform: linux-64
+@EXPLICIT
+https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81
+https://conda.anaconda.org/conda-forge/linux-64/libgomp-13.1.0-he5830b7_0.conda#56ca14d57ac29a75d23a39eb3ee0ddeb
+https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2#73aaf86a425cc6e73fcf236a5a46396d
+https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.1.0-he5830b7_0.conda#cd93f779ff018dd85c7544c015c9db3c
+https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.9-hd590300_0.conda#a0c6f0e7e1a467f5678f94dea18c8aa7
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+https://conda.anaconda.org/conda-forge/linux-64/aom-3.5.0-h27087fc_0.tar.bz2#a08150fd2298460cd1fcccf626305642
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+https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hbc2eb40_0.conda#38f84d395629e48b7c7b48a8ca740341
+https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-254-h3516f8a_0.conda#df4b1cd0c91b4234fb02b5701a4cdddc
+https://conda.anaconda.org/conda-forge/linux-64/xkeyboard-config-2.39-hd590300_0.conda#d88c7fc8a11858fb14761832e4da1954
+https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.5.0-h5d7e998_3.conda#c91ea308d7bf70b62ddda568478aa03b
+https://conda.anaconda.org/conda-forge/linux-64/libxslt-1.1.37-h0054252_1.conda#f27960e8873abb5476e96ef33bdbdccd
+https://conda.anaconda.org/conda-forge/linux-64/loguru-0.5.3-py310hff52083_3.tar.bz2#11284e9c52c8031649c5c9893d3e91ff
+https://conda.anaconda.org/conda-forge/linux-64/lxml-4.9.3-py310h9b7343a_0.conda#334f18dc35ace74076a93bbc7896fba6
+https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.0.9-pyhd8ed1ab_0.tar.bz2#e8fbc1b54b25f4b08281467bc13b70cc
+https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.7.2-py310hf38f957_0.conda#9b55c9041c5a7f80f184a2cb05ec9663
+https://conda.anaconda.org/conda-forge/noarch/ply-3.11-py_1.tar.bz2#7205635cd71531943440fbfe3b6b5727
+https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96
+https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.11-py310hc6cd4ac_0.conda#be1a7e420b7bac4ee02353d0e3161918
+https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_0.tar.bz2#f832c45a477c78bebd107098db465095
+https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.12.2-py310hc6cd4ac_4.conda#345beb10601d5360a15c033d68165a4f
+https://conda.anaconda.org/conda-forge/linux-64/mysql-common-8.0.33-hf1915f5_2.conda#a55ff0ed12efd86cf3a3dfb750adb950
+https://conda.anaconda.org/conda-forge/linux-64/mysql-libs-8.0.33-hca2cd23_2.conda#b2f09078f50b9e859aca3f0dc1cc8b7e
+https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1
+https://conda.anaconda.org/conda-forge/linux-64/nss-3.92-h1d7d5a4_0.conda#22c89a3d87828fe925b310b9cdf0f574
+https://conda.anaconda.org/conda-forge/linux-64/pulseaudio-client-16.1-hb77b528_4.conda#8f349ca16d30950aa00870484d9d30c4
+https://conda.anaconda.org/conda-forge/linux-64/xcb-util-0.4.0-hd590300_1.conda#9bfac7ccd94d54fd21a0501296d60424
+https://conda.anaconda.org/conda-forge/linux-64/xcb-util-image-0.4.0-h8ee46fc_1.conda#9d7bcddf49cbf727730af10e71022c73
+https://conda.anaconda.org/conda-forge/linux-64/xcb-util-keysyms-0.4.0-h8ee46fc_1.conda#632413adcd8bc16b515cab87a2932913
+https://conda.anaconda.org/conda-forge/linux-64/xcb-util-renderutil-0.3.9-hd590300_1.conda#e995b155d938b6779da6ace6c6b13816
+https://conda.anaconda.org/conda-forge/linux-64/xcb-util-wm-0.4.1-h8ee46fc_1.conda#90108a432fb5c6150ccfee3f03388656
+https://conda.anaconda.org/conda-forge/linux-64/xorg-xf86vidmodeproto-2.3.1-h7f98852_1002.tar.bz2#3ceea9668625c18f19530de98b15d5b0
+https://conda.anaconda.org/conda-forge/linux-64/qt-main-5.15.8-h7fe3ca9_15.conda#f09d307dd78e61e4eb2c6c2f81056d0e
+https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.15.9-py310h04931ad_4.conda#db878a0696f9a7980171fd3cf29cca22
+https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.7.2-py310hff52083_0.conda#7e454b4a61754714a4a4d183641374da
+https://conda.anaconda.org/conda-forge/noarch/napari-plugin-engine-0.2.0-pyhd8ed1ab_2.tar.bz2#4ebfbbcefe56d3f63ad1d3ca08db3a32
+https://conda.anaconda.org/conda-forge/linux-64/openslide-3.4.1-ha896ae7_9.conda#0bb0f558a4d7ff96ef996b53c7d05bae
+https://conda.anaconda.org/conda-forge/linux-64/openslide-python-1.3.0-py310h2372a71_0.conda#7d4cd67aa02983e268b3840985cd8105
+https://conda.anaconda.org/conda-forge/linux-64/numcodecs-0.11.0-py310heca2aa9_1.conda#476b14f637de9e4e811d881a925e8936
+https://conda.anaconda.org/conda-forge/noarch/zarr-2.14.2-pyhd8ed1ab_0.conda#0c5776fe65a12a421d7ddf90411a6c3f
+https://conda.anaconda.org/conda-forge/noarch/napari-lazy-openslide-0.3.0-pyhd8ed1ab_0.tar.bz2#7ebdc50c1c6b648d6d35c306bb5d95a4
+https://conda.anaconda.org/conda-forge/noarch/networkx-3.1-pyhd8ed1ab_0.conda#254f787d5068bc89f578bf63893ce8b4
+https://conda.anaconda.org/conda-forge/noarch/pint-0.22-pyhd8ed1ab_1.conda#a719c3f3959c529e558e9ed9f98c3f30
+https://conda.anaconda.org/conda-forge/linux-64/pydantic-1.10.12-py310h2372a71_1.conda#92818992253ff7f7ea4924785b1eadb9
+https://conda.anaconda.org/conda-forge/noarch/toposort-1.10-pyhd8ed1ab_0.conda#aeef653e20028f19a3c2cc70e166b509
+https://conda.anaconda.org/conda-forge/noarch/xsdata-23.8-pyhd8ed1ab_0.conda#5b5479ae38fe704be55192c6c1e611d7
+https://conda.anaconda.org/conda-forge/noarch/ome-types-0.4.2-pyhd8ed1ab_0.conda#011fe5e2b0c2c79c5d8114b720101a54
+https://conda.anaconda.org/conda-forge/noarch/wheel-0.41.2-pyhd8ed1ab_0.conda#1ccd092478b3e0ee10d7a891adbf8a4f
+https://conda.anaconda.org/conda-forge/noarch/pip-23.2.1-pyhd8ed1ab_0.conda#e2783aa3f9235225eec92f9081c5b801
+https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.4.1-py310h0a54255_0.conda#b9e952fe3f7528ab603d2776175ba8d2
+https://conda.anaconda.org/conda-forge/linux-64/scikit-image-0.19.3-py310h769672d_2.tar.bz2#c0391107b0cd0010708d6969ed759e8b
+https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.2.0-pyha21a80b_0.conda#978d03388b62173b8e6f79162cf52b86
+https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.3.0-py310hf7d194e_0.conda#3edb31043e22392f5174daca1c2514e9
+https://conda.anaconda.org/conda-forge/noarch/tqdm-4.66.1-pyhd8ed1ab_0.conda#03c97908b976498dcae97eb4e4f3149c
+https://conda.anaconda.org/conda-forge/noarch/yamale-4.0.4-pyh6c4a22f_0.tar.bz2#cc9f59f147740d88679bf1bd94dbe588
diff --git a/large/s3seg_qc.py b/large/s3seg_qc.py
index 710255b..1e323ce 100644
--- a/large/s3seg_qc.py
+++ b/large/s3seg_qc.py
@@ -7,6 +7,8 @@
 import tifffile
 import palom
 
+import _version
+
 
 def mask_to_bound(
     img_da,
@@ -86,11 +88,15 @@ def run_mcmicro(
     da_stack = da.array(out_channels)
 
     palom.pyramid.write_pyramid(
-        palom.pyramid.normalize_mosaics(da_stack),
+        [da_stack],
         out_path,
-        channel_names=channel_names,
+        channel_names=[channel_names],
         pixel_size=pixel_size,
-        downscale_factor=2
+        downscale_factor=2,
+        compression='zlib',
+        tile_size=1024,
+        save_RAM=True,
+        kwargs_tifffile=dict(software=f"s3segmenter-large v{_version.VERSION}")       
     )
 
     return 0
diff --git a/large/s3seg_util.py b/large/s3seg_util.py
deleted file mode 100644
index 69e4c17..0000000
--- a/large/s3seg_util.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import ome_types
-
-def detect_pixel_size(img_path):
-    try:
-        metadata = ome_types.from_tiff(img_path)
-        pixel_size = metadata.images[0].pixels.physical_size_x
-    except Exception as err:
-        print(err)
-        print()
-        print('Pixel size detection using ome-types failed')
-        pixel_size = None
-    return pixel_size
\ No newline at end of file
diff --git a/large/save_tifffile_pyramid.py b/large/save_tifffile_pyramid.py
deleted file mode 100644
index 95007b7..0000000
--- a/large/save_tifffile_pyramid.py
+++ /dev/null
@@ -1,117 +0,0 @@
-import numpy as np
-import tifffile
-import skimage.transform
-
-PHYSICAL_SIZE_UNIT = ['Ym', 'Zm', 'Em', 'Pm', 'Tm', 'Gm', 'Mm', 'km', 'hm', 'dam', 'm', 'dm', 'cm', 'mm', 'µm', 'nm', 'pm', 'fm', 'am', 'zm', 'ym', 'Å', 'thou', 'li', 'in', 'ft', 'yd', 'mi', 'ua', 'ly', 'pc', 'pt', 'pixel', 'reference frame']
-
-def normalize_image_shape(img):
-    assert img.ndim in (2, 3), (
-        'image must be 2D (Y, X) or 3D (C, Y, X)'
-    )
-    if img.ndim == 2:
-        img = img.reshape(1, *img.shape)
-    assert np.argmin(img.shape) == 0, (
-        '3D image must be in (C, Y, X) order'
-    )
-    return img
-
-def save_pyramid(
-    out_img, output_path,
-    pixel_sizes=(1, 1),
-    pixel_size_units=('µm', 'µm'),
-    channel_names=None,
-    software=None,
-    is_mask=False
-):
-    assert '.ome.tif' in str(output_path)
-    assert len(pixel_sizes) == len(pixel_size_units) == 2
-    assert out_img.ndim in (2, 3), (
-        'image must be either 2D (Y, X) or 3D (C, Y, X)'
-    )
-    
-    img_shape_ori = out_img.shape
-    out_img = normalize_image_shape(out_img)
-    img_shape = out_img.shape
-
-    size_x, size_y = np.array(pixel_sizes, dtype=float)
-    unit_x, unit_y = pixel_size_units
-
-    assert (unit_x in PHYSICAL_SIZE_UNIT) & (unit_y in PHYSICAL_SIZE_UNIT), (
-        f'pixel_size_units must be a tuple of the followings: '
-        f'{", ".join(PHYSICAL_SIZE_UNIT)}'
-    )
-
-    n_channels = img_shape[0]
-    if channel_names == None:
-        channel_names = [f'Channel {i}' for i in range(n_channels)]
-    else:
-        if type(channel_names) == str:
-            channel_names = [channel_names]
-        n_channel_names = len(channel_names)
-        assert n_channel_names == n_channels, (
-            f'number of channel_names ({n_channel_names}) must match '
-            f'number of channels ({n_channels})'
-        )
-    
-    if software == None:
-        software = ''
-        
-    metadata = {
-        'Creator': software,
-        'Pixels': {
-            'PhysicalSizeX': size_x,
-            'PhysicalSizeXUnit': unit_x,
-            'PhysicalSizeY': size_y,
-            'PhysicalSizeYUnit': unit_y,
-        },
-        'Channel': {'Name': channel_names},
-        
-    }
-
-    max_size = np.max(img_shape)
-    subifds = np.ceil(np.log2(max_size / 1024)).astype(int)
-    # skip pyramid if max image size <= 1024
-    if subifds < 0:
-        subifds = 0
-    
-    # use optimal tile size for disk space
-    tile_size = 16*np.ceil(
-        np.array(img_shape[1:]) / (2**subifds) / 16
-    ).astype(int)
-    options = {
-        'tile': tuple(tile_size)
-    }
-
-    with tifffile.TiffWriter(output_path, bigtiff=True) as tiff_out:
-        tiff_out.write(
-            data=out_img,
-            metadata=metadata,
-            software=software,
-            subifds=subifds,
-            **options
-        )
-        for i in range(subifds):
-            if i == 0:
-                down_2x_img = downsize_img_channels(out_img, is_mask=is_mask)
-            else:
-                down_2x_img = downsize_img_channels(down_2x_img, is_mask=is_mask)
-            tiff_out.write(
-                data=down_2x_img,
-                subfiletype=1,
-                **options
-            )
-
-    out_img = out_img.reshape(img_shape_ori)
-    return
-
-def downsize_channel(img, is_mask):
-    if is_mask:
-        return img[::2, ::2]
-    else:
-        return skimage.transform.downscale_local_mean(img, (2, 2)).astype(img.dtype)
-
-def downsize_img_channels(img, is_mask):
-    return np.array([
-        downsize_channel(c, is_mask=is_mask)
-        for c in img
-    ])
diff --git a/large/watershed.py b/large/watershed.py
index ea3a5b5..4b4c7c6 100644
--- a/large/watershed.py
+++ b/large/watershed.py
@@ -21,9 +21,10 @@
     format="%(asctime)s | %(levelname)-8s | %(message)s (%(filename)s:%(lineno)s)", 
     datefmt="%Y-%m-%d %H:%M:%S", 
     level=logging.INFO 
-) 
+)
+
+import _version
  
-import save_tifffile_pyramid 
  
 def filter_label_area(label_img, area_min, area_max): 
     if np.all(label_img == 0): 
@@ -120,21 +121,27 @@ def run(self, config_id=None, compute=True):
     def write(self, file_path, img=None, config_id=None): 
         file_path = pathlib.Path(file_path) 
         file_name = file_path.name 
+        if img is None: img = self.run(config_id, compute=False) 
         if file_name.endswith('.zarr'): 
-            if img is None: img = self.run(config_id, compute=False) 
             logging.info(f'Writing to {file_path}') 
             with dask.diagnostics.ProgressBar(): 
                 return img.to_zarr(file_path) 
         if file_name.endswith(('.ome.tiff', '.ome.tif')): 
-            if img is None: img = self.run(config_id) 
-            logging.info(f'Writing to {file_path}') 
+            logging.info(f'Writing to {file_path}')
+            pixel_size = self.pixel_size
             if self.pixel_size is None:
-                pixel_sizes = (1, 1)
-            else: 
-                pixel_sizes = (self.pixel_size, self.pixel_size)
-            return save_tifffile_pyramid.save_pyramid( 
-                img, file_path, is_mask=True, pixel_sizes=pixel_sizes
-            ) 
+                pixel_size = 1
+            return palom.pyramid.write_pyramid(
+                [img],
+                file_path,
+                pixel_size=pixel_size,
+                downscale_factor=2,
+                compression='zlib',
+                is_mask=True,
+                tile_size=1024,
+                save_RAM=True,
+                kwargs_tifffile=dict(software=f"s3segmenter-large v{_version.VERSION}")
+            )
         logging.warning('Write failed: output file type not supported') 
         return 
      
@@ -406,10 +413,14 @@ def main(argv=sys.argv):
 
     pixel_size = args.pixel_size
     if pixel_size is None:
-        pixel_size = 1.0
-        logging.warning(
-            f"Pixel size not specified, using {pixel_size} micron as a placeholder"
-        ) 
+        try:
+            pixel_size = palom.reader.OmePyramidReader(args.i).pixel_size
+        except Exception as err:
+            print(err)
+            pixel_size = 1.0
+            logging.warning(
+                f"Pixel size not specified, using {pixel_size} µm as a placeholder"
+            ) 
  
     segmentor = WatershedSegmentor( 
         da.from_array(probability_maps[1], chunks=2048), 
@@ -473,12 +484,11 @@ def expand_mask_from_file(
         tifffile.imread(input_path, aszarr=True, series=0, level=0) 
     ) 
      
-    with dask.diagnostics.ProgressBar(): 
-        segmentor.write_expanded( 
-            expanded_path, 
-            expand_size, 
-            da.from_zarr(z).rechunk(2048) 
-        ) 
+    segmentor.write_expanded( 
+        expanded_path, 
+        expand_size, 
+        da.from_zarr(z).rechunk(2048) 
+    ) 
     return expanded_path 
  
 def difference_mask_from_file(
@@ -508,11 +518,10 @@ def difference_mask_from_file(
     segmentor = WatershedSegmentor( 
         None, None, pixel_size=pixel_size
     ) 
-    with dask.diagnostics.ProgressBar(): 
-        segmentor.write( 
-            file_path=output_path, 
-            img=out_mask 
-        ) 
+    segmentor.write( 
+        file_path=output_path, 
+        img=out_mask 
+    ) 
     return