From 3aeb70d42b9620c2fc95c22ac74dd65017207a27 Mon Sep 17 00:00:00 2001
From: Youssef Assis <ysf.assis@gmail.com>
Date: Tue, 29 Sep 2026 20:44:49 +0200
Subject: [PATCH 1/7] [FEAT] Install the pipeline steps as commands

The scripts and their configs only worked from a clone. They now live
in the package (deepaneseg/cli, deepaneseg/configs) and are installed
as commands: deepaneseg-remove-skull, -extract-points, -preprocess,
-train, -predict and -evaluate. `pip install git+<repo>` is enough to
run them from any folder. configs/ is a package so that Hydra finds
it once installed. Existing tests now run the modules instead of the
script paths; the Docker image and CI use the commands.
---
 .github/workflows/ci.yml                          |  2 +-
 Dockerfile                                        |  2 +-
 deepaneseg/cli/__init__.py                        |  0
 {scripts => deepaneseg/cli}/evaluate.py           |  5 ++---
 {scripts => deepaneseg/cli}/extract_points.py     |  3 +--
 {scripts => deepaneseg/cli}/predict.py            |  7 +++----
 .../cli/preprocess.py                             |  5 ++---
 {scripts => deepaneseg/cli}/remove_skull.py       |  5 ++---
 {scripts => deepaneseg/cli}/train.py              |  5 ++---
 deepaneseg/configs/__init__.py                    |  1 +
 .../configs}/augmentation/default.yaml            |  0
 {configs => deepaneseg/configs}/data/default.yaml |  4 ++--
 {configs => deepaneseg/configs}/evaluate.yaml     |  4 ++--
 .../configs}/extract_points.yaml                  |  0
 {configs => deepaneseg/configs}/model/unet3d.yaml |  0
 .../configs}/optimizer/adam.yaml                  |  0
 {configs => deepaneseg/configs}/predict.yaml      |  4 ++--
 {configs => deepaneseg/configs}/preprocess.yaml   |  0
 {configs => deepaneseg/configs}/remove_skull.yaml |  0
 .../configs}/scheduler/reduce_lr_on_plateau.yaml  |  0
 {configs => deepaneseg/configs}/train.yaml        |  0
 pyproject.toml                                    | 11 +++++++++++
 tests/test_config.py                              |  2 +-
 tests/test_evaluate.py                            |  7 ++-----
 tests/test_predict_scans.py                       |  4 ++--
 tests/test_reproducibility.py                     |  4 +---
 tests/test_scripts.py                             | 15 +++++++--------
 27 files changed, 45 insertions(+), 45 deletions(-)
 create mode 100644 deepaneseg/cli/__init__.py
 rename {scripts => deepaneseg/cli}/evaluate.py (94%)
 rename {scripts => deepaneseg/cli}/extract_points.py (81%)
 rename {scripts => deepaneseg/cli}/predict.py (91%)
 rename scripts/preprocess_data.py => deepaneseg/cli/preprocess.py (85%)
 rename {scripts => deepaneseg/cli}/remove_skull.py (87%)
 rename {scripts => deepaneseg/cli}/train.py (94%)
 create mode 100644 deepaneseg/configs/__init__.py
 rename {configs => deepaneseg/configs}/augmentation/default.yaml (100%)
 rename {configs => deepaneseg/configs}/data/default.yaml (77%)
 rename {configs => deepaneseg/configs}/evaluate.yaml (87%)
 rename {configs => deepaneseg/configs}/extract_points.yaml (100%)
 rename {configs => deepaneseg/configs}/model/unet3d.yaml (100%)
 rename {configs => deepaneseg/configs}/optimizer/adam.yaml (100%)
 rename {configs => deepaneseg/configs}/predict.yaml (87%)
 rename {configs => deepaneseg/configs}/preprocess.yaml (100%)
 rename {configs => deepaneseg/configs}/remove_skull.yaml (100%)
 rename {configs => deepaneseg/configs}/scheduler/reduce_lr_on_plateau.yaml (100%)
 rename {configs => deepaneseg/configs}/train.yaml (100%)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index a3ed70e..9e72847 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -35,4 +35,4 @@ jobs:
       - name: Smoke test
         run: |
           docker run --rm deepaneseg:cpu python -c "import torch, deepaneseg.utils; print(torch.__version__)"
-          docker run --rm deepaneseg:cpu python scripts/train.py --help
+          docker run --rm deepaneseg:cpu deepaneseg-train --help
diff --git a/Dockerfile b/Dockerfile
index 5a05243..427424a 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -21,4 +21,4 @@ RUN uv sync --locked --no-install-project --no-dev --extra ${TORCH} && rm -rf /r
 COPY . .
 RUN uv sync --locked --no-dev --extra ${TORCH} && rm -rf /root/.cache/uv
 
-CMD ["python", "scripts/train.py", "--help"]
+CMD ["deepaneseg-train", "--help"]
diff --git a/deepaneseg/cli/__init__.py b/deepaneseg/cli/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/scripts/evaluate.py b/deepaneseg/cli/evaluate.py
similarity index 94%
rename from scripts/evaluate.py
rename to deepaneseg/cli/evaluate.py
index 1168e42..d845995 100644
--- a/scripts/evaluate.py
+++ b/deepaneseg/cli/evaluate.py
@@ -1,7 +1,6 @@
-#!/usr/bin/env python3
-"""Score a training's predictions on its test patients; configuration in configs/evaluate.yaml.
+"""Score a training's predictions on its test patients; configuration in deepaneseg/configs/evaluate.yaml.
 
-Example: python scripts/evaluate.py train_dir=/data/0Work/exp1
+Example: deepaneseg-evaluate train_dir=/data/0Work/exp1
 A detection (connected component above the threshold) is a true positive when its center lies within an
 aneurysm's radius (ADAM challenge criteria). Writes per_patient.csv and summary.json to <train_dir>/evaluation.
 """
diff --git a/scripts/extract_points.py b/deepaneseg/cli/extract_points.py
similarity index 81%
rename from scripts/extract_points.py
rename to deepaneseg/cli/extract_points.py
index 571e5ba..b08eef4 100644
--- a/scripts/extract_points.py
+++ b/deepaneseg/cli/extract_points.py
@@ -1,7 +1,6 @@
-#!/usr/bin/env python3
 """Select negative patch centers (on vessels and in the parenchyma) for every patient.
 
-Configuration in configs/extract_points.yaml. Example: python scripts/extract_points.py data_dir=/data
+Configuration in deepaneseg/configs/extract_points.yaml. Example: deepaneseg-extract-points data_dir=/data
 Writes <output> (CSV) and its 3D Slicer .fcsv counterpart in each patient folder.
 Requires the 'noskull volume' produced by remove_skull.py.
 """
diff --git a/scripts/predict.py b/deepaneseg/cli/predict.py
similarity index 91%
rename from scripts/predict.py
rename to deepaneseg/cli/predict.py
index 30d952f..ed5bf67 100755
--- a/scripts/predict.py
+++ b/deepaneseg/cli/predict.py
@@ -1,9 +1,8 @@
-#!/usr/bin/env python3
-"""Predict with a trained model; configuration in configs/predict.yaml.
+"""Predict with a trained model; configuration in deepaneseg/configs/predict.yaml.
 
 Examples:
-    python scripts/predict.py train_dir=/data/0Work/exp1                      # test patients of the split
-    python scripts/predict.py train_dir=/data/0Work/exp1 input=scan.nii.gz    # any scan, or a folder of scans
+    deepaneseg-predict train_dir=/data/0Work/exp1                      # test patients of the split
+    deepaneseg-predict train_dir=/data/0Work/exp1 input=scan.nii.gz    # any scan, or a folder of scans
 Data and model settings come from the training's resolved configuration (<train_dir>/.hydra/config.yaml).
 For each case, writes the probability map <name>.nii.gz and the detections <name>_detections.csv (and .fcsv,
 for 3D Slicer).
diff --git a/scripts/preprocess_data.py b/deepaneseg/cli/preprocess.py
similarity index 85%
rename from scripts/preprocess_data.py
rename to deepaneseg/cli/preprocess.py
index 42d3347..d4c2a78 100644
--- a/scripts/preprocess_data.py
+++ b/deepaneseg/cli/preprocess.py
@@ -1,7 +1,6 @@
-#!/usr/bin/env python3
-"""Split the patients into training/validation/testing sets; configuration in configs/preprocess.yaml.
+"""Split the patients into training/validation/testing sets; configuration in deepaneseg/configs/preprocess.yaml.
 
-Example: python scripts/preprocess_data.py data_dir=/data split=[0.7,0.2,0.1]
+Example: deepaneseg-preprocess data_dir=/data split=[0.7,0.2,0.1]
 Writes the split to data.split_file (<data_dir>/0Work/split_pats.json by default).
 """
 
diff --git a/scripts/remove_skull.py b/deepaneseg/cli/remove_skull.py
similarity index 87%
rename from scripts/remove_skull.py
rename to deepaneseg/cli/remove_skull.py
index 2a04a68..8753243 100644
--- a/scripts/remove_skull.py
+++ b/deepaneseg/cli/remove_skull.py
@@ -1,7 +1,6 @@
-#!/usr/bin/env python3
-"""Skull-strip every patient volume; configuration in configs/remove_skull.yaml.
+"""Skull-strip every patient volume; configuration in deepaneseg/configs/remove_skull.yaml.
 
-Example: python scripts/remove_skull.py data_dir=/data
+Example: deepaneseg-remove-skull data_dir=/data
 The result is saved next to each volume and registered as 'noskull volume' in the patient's config.json.
 """
 
diff --git a/scripts/train.py b/deepaneseg/cli/train.py
similarity index 94%
rename from scripts/train.py
rename to deepaneseg/cli/train.py
index 7c08457..53c5cc1 100755
--- a/scripts/train.py
+++ b/deepaneseg/cli/train.py
@@ -1,7 +1,6 @@
-#!/usr/bin/env python3
-"""Train a model; configuration in configs/train.yaml.
+"""Train a model; configuration in deepaneseg/configs/train.yaml.
 
-Example: python scripts/train.py data_dir=/data name=exp1 batch_size=8
+Example: deepaneseg-train data_dir=/data name=exp1 batch_size=8
 Checkpoints, TensorBoard logs and the resolved configuration (.hydra/) are written to <data_dir>/0Work/<name>.
 Running the same command again resumes from the last checkpoint.
 """
diff --git a/deepaneseg/configs/__init__.py b/deepaneseg/configs/__init__.py
new file mode 100644
index 0000000..bf4c8be
--- /dev/null
+++ b/deepaneseg/configs/__init__.py
@@ -0,0 +1 @@
+"""Hydra configuration of the deepaneseg commands (a package so that installed commands find it)."""
diff --git a/configs/augmentation/default.yaml b/deepaneseg/configs/augmentation/default.yaml
similarity index 100%
rename from configs/augmentation/default.yaml
rename to deepaneseg/configs/augmentation/default.yaml
diff --git a/configs/data/default.yaml b/deepaneseg/configs/data/default.yaml
similarity index 77%
rename from configs/data/default.yaml
rename to deepaneseg/configs/data/default.yaml
index 6dfcec3..5a33f7b 100644
--- a/configs/data/default.yaml
+++ b/deepaneseg/configs/data/default.yaml
@@ -1,9 +1,9 @@
 # Patient data and patch geometry
 data_dir: ${data_dir}                    # directory containing the patient folders (P????)
 work_dir: ${.data_dir}/0Work             # split file and trainings are written here
-split_file: ${.work_dir}/split_pats.json # written by preprocess_data.py
+split_file: ${.work_dir}/split_pats.json # written by deepaneseg-preprocess
 volume: init volume                      # key, in each patient's config.json, of the volume to train on
 normalize: Linear                        # Linear (rescale to [0, 1]), Normal (zero mean, unit variance) or null
-negative_patch_centers: points.csv       # written by extract_points.py in each patient folder
+negative_patch_centers: points.csv       # written by deepaneseg-extract-points in each patient folder
 patch_shape: [48, 48, 48]                # patch dimension, in voxels
 patch_size: [38, 38, 38]                 # patch size, in mm (resolution = patch_size / patch_shape)
diff --git a/configs/evaluate.yaml b/deepaneseg/configs/evaluate.yaml
similarity index 87%
rename from configs/evaluate.yaml
rename to deepaneseg/configs/evaluate.yaml
index 42d6a45..b5b32aa 100644
--- a/configs/evaluate.yaml
+++ b/deepaneseg/configs/evaluate.yaml
@@ -1,6 +1,6 @@
 # Detection metrics (ADAM challenge criteria) of a training's predictions on its test patients
-train_dir: ???                            # training directory written by train.py
-predictions: ${train_dir}/prediction/test # <patient>.nii.gz probability maps written by predict.py
+train_dir: ???                            # training directory written by deepaneseg-train
+predictions: ${train_dir}/prediction/test # <patient>.nii.gz probability maps written by deepaneseg-predict
 threshold: 0.5                            # probability above which a voxel belongs to a detection
 min_size: null                            # detections smaller than this, in voxels, are ignored; null keeps all
 
diff --git a/configs/extract_points.yaml b/deepaneseg/configs/extract_points.yaml
similarity index 100%
rename from configs/extract_points.yaml
rename to deepaneseg/configs/extract_points.yaml
diff --git a/configs/model/unet3d.yaml b/deepaneseg/configs/model/unet3d.yaml
similarity index 100%
rename from configs/model/unet3d.yaml
rename to deepaneseg/configs/model/unet3d.yaml
diff --git a/configs/optimizer/adam.yaml b/deepaneseg/configs/optimizer/adam.yaml
similarity index 100%
rename from configs/optimizer/adam.yaml
rename to deepaneseg/configs/optimizer/adam.yaml
diff --git a/configs/predict.yaml b/deepaneseg/configs/predict.yaml
similarity index 87%
rename from configs/predict.yaml
rename to deepaneseg/configs/predict.yaml
index 422bc3a..26f0156 100644
--- a/configs/predict.yaml
+++ b/deepaneseg/configs/predict.yaml
@@ -1,10 +1,10 @@
 # Predicts with a trained model; data and model settings are read from the training's config
-train_dir: ???                    # training directory written by train.py
+train_dir: ???                    # training directory written by deepaneseg-train
 input: null                       # NIfTI scan or folder of scans; null: the test patients of the training's split
 output_dir: null                  # null: <train_dir>/prediction/test, or <train_dir>/prediction/scans for input
 checkpoint: last_checkpoint.pytorch
 volume: noskull volume            # key, in each patient's config.json, of the volume to predict on
-skull_strip: true                 # skull-strip input scans as remove_skull.py does, to match volume above
+skull_strip: true                 # skull-strip input scans as deepaneseg-remove-skull does, to match volume above
 threshold: 0.5                    # detections: connected components above this probability
 min_size: null                    # detections smaller than this, in voxels, are ignored; null keeps all
 margin: 8                         # voxels discarded at each patch border when stitching predictions
diff --git a/configs/preprocess.yaml b/deepaneseg/configs/preprocess.yaml
similarity index 100%
rename from configs/preprocess.yaml
rename to deepaneseg/configs/preprocess.yaml
diff --git a/configs/remove_skull.yaml b/deepaneseg/configs/remove_skull.yaml
similarity index 100%
rename from configs/remove_skull.yaml
rename to deepaneseg/configs/remove_skull.yaml
diff --git a/configs/scheduler/reduce_lr_on_plateau.yaml b/deepaneseg/configs/scheduler/reduce_lr_on_plateau.yaml
similarity index 100%
rename from configs/scheduler/reduce_lr_on_plateau.yaml
rename to deepaneseg/configs/scheduler/reduce_lr_on_plateau.yaml
diff --git a/configs/train.yaml b/deepaneseg/configs/train.yaml
similarity index 100%
rename from configs/train.yaml
rename to deepaneseg/configs/train.yaml
diff --git a/pyproject.toml b/pyproject.toml
index c921302..e480777 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,6 +24,14 @@ dependencies = [
     "hydra-core>=1.3",
 ]
 
+[project.scripts]
+deepaneseg-remove-skull = "deepaneseg.cli.remove_skull:main"
+deepaneseg-extract-points = "deepaneseg.cli.extract_points:main"
+deepaneseg-preprocess = "deepaneseg.cli.preprocess:main"
+deepaneseg-train = "deepaneseg.cli.train:main"
+deepaneseg-predict = "deepaneseg.cli.predict:main"
+deepaneseg-evaluate = "deepaneseg.cli.evaluate:main"
+
 [project.optional-dependencies]
 # PyTorch build, for uv: `uv sync --extra cpu` or `uv sync --extra cu126` (see [tool.uv.sources])
 cpu = ["torch"]
@@ -54,6 +62,9 @@ explicit = true
 [tool.setuptools.packages.find]
 include = ["deepaneseg*"]
 
+[tool.setuptools.package-data]
+deepaneseg = ["configs/*.yaml", "configs/*/*.yaml"]
+
 [tool.pytest.ini_options]
 testpaths = ["tests"]
 
diff --git a/tests/test_config.py b/tests/test_config.py
index 4408ed4..8d44417 100644
--- a/tests/test_config.py
+++ b/tests/test_config.py
@@ -11,7 +11,7 @@
 from deepaneseg.training.trainer import create_trainer
 from deepaneseg.utils import get_model
 
-CONFIG_DIR = str(Path(__file__).parents[1] / "configs")
+CONFIG_DIR = str(Path(__file__).parents[1] / "deepaneseg" / "configs")
 
 
 def compose_config(name, overrides):
diff --git a/tests/test_evaluate.py b/tests/test_evaluate.py
index 41733f5..85a930e 100644
--- a/tests/test_evaluate.py
+++ b/tests/test_evaluate.py
@@ -1,7 +1,6 @@
 import json
 import subprocess
 import sys
-from pathlib import Path
 
 import nibabel as ni
 import numpy as np
@@ -12,8 +11,6 @@
 from deepaneseg.data.io import read_aneurysm_spheres
 from deepaneseg.inference.evaluation import summarize_detections
 
-SCRIPTS = Path(__file__).parents[1] / "scripts"
-
 
 def make_patient(data_dir, name, aneurysm_points=None):
     patient = data_dir / name
@@ -66,7 +63,7 @@ def test_evaluate_script_scores_the_test_predictions(tmp_path):
     ni.save(ni.Nifti1Image(np.full((20, 20, 20), 0.1), np.eye(4)), predictions / "P0002.nii.gz")
 
     result = subprocess.run(
-        [sys.executable, str(SCRIPTS / "evaluate.py"), f"train_dir={train_dir}"], capture_output=True, text=True
+        [sys.executable, "-m", "deepaneseg.cli.evaluate", f"train_dir={train_dir}"], capture_output=True, text=True
     )
 
     assert result.returncode == 0, result.stderr[-2000:]
@@ -87,7 +84,7 @@ def test_evaluate_script_reports_missing_predictions(tmp_path):
     )
 
     result = subprocess.run(
-        [sys.executable, str(SCRIPTS / "evaluate.py"), f"train_dir={train_dir}"], capture_output=True, text=True
+        [sys.executable, "-m", "deepaneseg.cli.evaluate", f"train_dir={train_dir}"], capture_output=True, text=True
     )
 
     assert result.returncode != 0 and "P0001" in result.stderr
diff --git a/tests/test_predict_scans.py b/tests/test_predict_scans.py
index a6e7e5c..59983aa 100644
--- a/tests/test_predict_scans.py
+++ b/tests/test_predict_scans.py
@@ -47,7 +47,7 @@ def test_save_detections_writes_csv_and_slicer_markups(tmp_path):
 @pytest.fixture
 def trained(tmp_path):
     """A tiny untrained model with its saved training configuration."""
-    with initialize_config_dir(config_dir=str(ROOT / "configs"), version_base="1.3"):
+    with initialize_config_dir(config_dir=str(ROOT / "deepaneseg" / "configs"), version_base="1.3"):
         cfg = compose(
             "train",
             [
@@ -67,7 +67,7 @@ def trained(tmp_path):
 
 def predict(*args):
     return subprocess.run(
-        [sys.executable, str(ROOT / "scripts" / "predict.py"), *map(str, args)], capture_output=True, text=True
+        [sys.executable, "-m", "deepaneseg.cli.predict", *map(str, args)], capture_output=True, text=True
     )
 
 
diff --git a/tests/test_reproducibility.py b/tests/test_reproducibility.py
index 61e2ebc..06ba285 100644
--- a/tests/test_reproducibility.py
+++ b/tests/test_reproducibility.py
@@ -14,8 +14,6 @@
 from deepaneseg.data.io import fetch_patient_dirs
 from deepaneseg.utils import run_info, seed_everything
 
-SCRIPTS = Path(__file__).parents[1] / "scripts"
-
 
 def test_split_depends_only_on_the_seed():
     patients = [f"P{i:04d}" for i in range(20)]
@@ -89,7 +87,7 @@ def train(name):
             "data.patch_size=[16,16,16]",
             "augmentation.positive.duplicates=2",
         ]
-        result = subprocess.run([sys.executable, str(SCRIPTS / "train.py"), *args], capture_output=True, text=True)
+        result = subprocess.run([sys.executable, "-m", "deepaneseg.cli.train", *args], capture_output=True, text=True)
         assert result.returncode == 0, result.stderr[-2000:]
         return torch.load(tmp_path / "0Work" / name / "last_checkpoint.pytorch")["model_state_dict"]
 
diff --git a/tests/test_scripts.py b/tests/test_scripts.py
index c701ed6..b243e26 100644
--- a/tests/test_scripts.py
+++ b/tests/test_scripts.py
@@ -1,13 +1,12 @@
 import json
 import subprocess
 import sys
-from pathlib import Path
 
-SCRIPTS = Path(__file__).parents[1] / "scripts"
 
-
-def run(script, *args):
-    return subprocess.run([sys.executable, str(SCRIPTS / script), *map(str, args)], capture_output=True, text=True)
+def run(command, *args):
+    return subprocess.run(
+        [sys.executable, "-m", f"deepaneseg.cli.{command}", *map(str, args)], capture_output=True, text=True
+    )
 
 
 def make_patients(data_dir, n):
@@ -19,8 +18,8 @@ def make_patients(data_dir, n):
 def test_preprocess_data_splits_patients_and_refuses_to_overwrite(tmp_path):
     make_patients(tmp_path, 10)
 
-    first = run("preprocess_data.py", f"data_dir={tmp_path}")
-    second = run("preprocess_data.py", f"data_dir={tmp_path}")
+    first = run("preprocess", f"data_dir={tmp_path}")
+    second = run("preprocess", f"data_dir={tmp_path}")
 
     assert first.returncode == 0, first.stderr
     split = json.loads((tmp_path / "0Work" / "split_pats.json").read_text())
@@ -29,6 +28,6 @@ def test_preprocess_data_splits_patients_and_refuses_to_overwrite(tmp_path):
 
 
 def test_train_requires_data_dir_and_name():
-    result = run("train.py")
+    result = run("train")
 
     assert result.returncode != 0 and "Missing mandatory value" in result.stderr

From 09f34b208f537b41ae4b5d5b52f86c248aabcb81 Mon Sep 17 00:00:00 2001
From: Youssef Assis <ysf.assis@gmail.com>
Date: Tue, 29 Sep 2026 20:45:19 +0200
Subject: [PATCH 2/7] [TEST] Check the installed commands and their packaged
 configs

---
 tests/test_cli.py | 36 ++++++++++++++++++++++++++++++++++++
 1 file changed, 36 insertions(+)
 create mode 100644 tests/test_cli.py

diff --git a/tests/test_cli.py b/tests/test_cli.py
new file mode 100644
index 0000000..62faa05
--- /dev/null
+++ b/tests/test_cli.py
@@ -0,0 +1,36 @@
+"""The installed commands and the configs they read."""
+
+import shutil
+import subprocess
+from importlib.metadata import entry_points
+from importlib.resources import files
+
+import pytest
+
+COMMANDS = {
+    "deepaneseg-remove-skull": "remove_skull",
+    "deepaneseg-extract-points": "extract_points",
+    "deepaneseg-preprocess": "preprocess",
+    "deepaneseg-train": "train",
+    "deepaneseg-predict": "predict",
+    "deepaneseg-evaluate": "evaluate",
+}
+
+
+def test_every_command_is_installed():
+    installed = {ep.name: ep for ep in entry_points(group="console_scripts") if ep.name.startswith("deepaneseg-")}
+
+    assert set(installed) == set(COMMANDS)
+    assert all(callable(ep.load()) for ep in installed.values())
+
+
+@pytest.mark.parametrize("config", sorted(set(COMMANDS.values())))
+def test_every_command_has_its_config_in_the_package(config):
+    assert (files("deepaneseg.configs") / f"{config}.yaml").is_file()
+
+
+@pytest.mark.parametrize("command", ["deepaneseg-train", "deepaneseg-predict"])
+def test_commands_run_from_any_folder(command, tmp_path):
+    result = subprocess.run([shutil.which(command), "--help"], cwd=tmp_path, capture_output=True, text=True)
+
+    assert result.returncode == 0 and "Configuration groups" in result.stdout

From bef15322d99fa893a48a62c07e9eed500993c276 Mon Sep 17 00:00:00 2001
From: Youssef Assis <ysf.assis@gmail.com>
Date: Tue, 29 Sep 2026 20:45:20 +0200
Subject: [PATCH 3/7] [CHORE] Check in CI that the built wheel runs outside the
 repository

---
 .github/workflows/ci.yml | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 9e72847..de11514 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -26,6 +26,24 @@ jobs:
       - name: Test
         run: uv run pytest
 
+  wheel:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/setup-uv@v10.2.0
+      - name: Build and install the wheel outside the repository
+        run: |
+          uv build --wheel -o /tmp/dist
+          uv venv /tmp/venv
+          VIRTUAL_ENV=/tmp/venv uv pip install /tmp/dist/*.whl --extra-index-url https://download.pytorch.org/whl/cpu --index-strategy unsafe-best-match
+      - name: Run the commands from another folder
+        working-directory: /tmp
+        run: |
+          mkdir -p data/P0001 && echo '{}' > data/P0001/config.json
+          /tmp/venv/bin/deepaneseg-train --help
+          /tmp/venv/bin/deepaneseg-preprocess data_dir=data
+          test -f data/0Work/split_pats.json
+
   docker:
     runs-on: ubuntu-latest
     steps:

From 72a207ee6565b013bbd6d3a621f78d6934c0d618 Mon Sep 17 00:00:00 2001
From: Youssef Assis <ysf.assis@gmail.com>
Date: Tue, 29 Sep 2026 20:45:40 +0200
Subject: [PATCH 4/7] [DOCS] Use the installed commands in the README

---
 README.md | 40 +++++++++++++++++++++-------------------
 1 file changed, 21 insertions(+), 19 deletions(-)

diff --git a/README.md b/README.md
index 2e6a625..b7b0fd2 100644
--- a/README.md
+++ b/README.md
@@ -25,9 +25,9 @@ deepaneseg/
   training/   dataset, losses, metrics, trainer
   inference/  patch-wise prediction and ADAM-style evaluation
   experimental/  post-paper models (vessel-coupled U-Nets) and their trainer
-configs/      Hydra configuration (see Configuration)
-scripts/      pipeline steps (see Usage)
-  experimental/  analysis scripts from the PhD experiments
+  cli/        pipeline commands (see Usage)
+  configs/    Hydra configuration (see Configuration)
+scripts/experimental/  analysis scripts from the PhD experiments
 ```
 
 The data is expected in the following layout:
@@ -48,56 +48,57 @@ Data_dir/
 ```
 
 # Configuration
-Settings are [Hydra](https://hydra.cc) configs in `configs/`: one file per script (`train.yaml`, `predict.yaml`,
-`preprocess.yaml`, `remove_skull.yaml`, `extract_points.yaml`) composed from groups (`data`, `model`, `optimizer`,
+Settings are [Hydra](https://hydra.cc) configs in `deepaneseg/configs/`: one file per command (`train.yaml`,
+`predict.yaml`, `evaluate.yaml`, `preprocess.yaml`, `remove_skull.yaml`, `extract_points.yaml`) composed from groups (`data`, `model`, `optimizer`,
 `scheduler`, `augmentation`). Any value can be overridden on the command line:
 ```
-uv run python scripts/train.py data_dir=Data_dir name=my_training batch_size=8 optimizer.lr=3e-4 model.f_maps=32
-uv run python scripts/train.py --cfg job --resolve data_dir=Data_dir name=my_training   # print the config
+uv run deepaneseg-train data_dir=Data_dir name=my_training batch_size=8 optimizer.lr=3e-4 model.f_maps=32
+uv run deepaneseg-train --cfg job --resolve data_dir=Data_dir name=my_training   # print the config
 ```
 Runs are reproducible: `seed` (0 by default) fixes the patient split and the training, and each training writes
 `run_info.json` with the git commit and library versions next to its checkpoints. For identical GPU runs, also set
 `deterministic=true` (slower).
 
 To add an alternative (for example another scheduler), add a file to the group, such as
-`configs/scheduler/step.yaml` with a `_target_` class, and select it with `scheduler=step`.
+`deepaneseg/configs/scheduler/step.yaml` with a `_target_` class, and select it with `scheduler=step`.
 
 # Usage
-Run every command from the repository root.
+Run the commands with `uv run` from the repository folder, or directly (`deepaneseg-train …`) from any folder once
+`.venv` is activated or the package is installed with pip.
 
 **1. Data preparation:** skull-strip the volumes, then select the negative patch centers.
 ```
-uv run python scripts/remove_skull.py data_dir=Data_dir
-uv run python scripts/extract_points.py data_dir=Data_dir
+uv run deepaneseg-remove-skull data_dir=Data_dir
+uv run deepaneseg-extract-points data_dir=Data_dir
 ```
 
 **2. Split:** assign the patients to training, validation and testing.
 ```
-uv run python scripts/preprocess_data.py data_dir=Data_dir          # split=[0.7,0.2,0.1] overwrite=true
+uv run deepaneseg-preprocess data_dir=Data_dir          # split=[0.7,0.2,0.1] overwrite=true
 ```
 
 **3. Training:** outputs go to `Data_dir/0Work/my_training`; running the same command again resumes training.
 The training and validation volumes are kept in memory as float32, about 4 bytes per voxel (150 MiB for a
 512×512×150 scan).
 ```
-uv run python scripts/train.py data_dir=Data_dir name=my_training
+uv run deepaneseg-train data_dir=Data_dir name=my_training
 ```
 
 **4. Testing:** predictions of the test patients are written to `Data_dir/0Work/my_training/prediction/test`: a
 probability map per patient, and its detections (center and radius in mm, size, probability) as CSV and as `.fcsv`
 markups for 3D Slicer.
 ```
-uv run python scripts/predict.py train_dir=Data_dir/0Work/my_training
+uv run deepaneseg-predict train_dir=Data_dir/0Work/my_training
 ```
 Any other scan, or folder of scans, can be predicted the same way (skull-stripped first, like the training data):
 ```
-uv run python scripts/predict.py train_dir=Data_dir/0Work/my_training input=scan.nii.gz output_dir=results
+uv run deepaneseg-predict train_dir=Data_dir/0Work/my_training input=scan.nii.gz output_dir=results
 ```
 
 **5. Evaluation:** detection metrics of the test predictions (ADAM challenge criteria: sensitivity and false
 positives per patient), written to `Data_dir/0Work/my_training/evaluation`.
 ```
-uv run python scripts/evaluate.py train_dir=Data_dir/0Work/my_training    # threshold=0.5 min_size=null
+uv run deepaneseg-evaluate train_dir=Data_dir/0Work/my_training    # threshold=0.5 min_size=null
 ```
 
 # Differences from the published code
@@ -111,7 +112,8 @@ uv sync --extra cu126    # NVIDIA GPU (CUDA 12.6)
 uv sync --extra cpu      # CPU only (also the build used on macOS)
 ```
 This creates `.venv` with the exact versions of `uv.lock`, including pytest and ruff. Commands then run with
-`uv run`, e.g. `uv run pytest`. Without uv, `pip install -e .` also works (PyTorch's default PyPI build).
+`uv run`, e.g. `uv run pytest`. Without uv, `pip install -e .` also works (PyTorch's default PyPI build), or, to only
+use the commands, `pip install git+https://github.com/youssefassis/DeepAneSeg`.
 
 # Docker
 Build the image with the GPU (default) or CPU PyTorch build:
@@ -122,9 +124,9 @@ docker build -t deepaneseg:cpu --build-arg TORCH=cpu .
 Mount the data folder and run any script; outputs are written under `Data_dir/0Work` on the host:
 ```
 docker run --rm --gpus all --shm-size=8g -v /path/to/Data_dir:/data deepaneseg \
-    python scripts/train.py data_dir=/data name=my_training
+    deepaneseg-train data_dir=/data name=my_training
 docker run --rm --gpus all --shm-size=8g -v /path/to/Data_dir:/data deepaneseg \
-    python scripts/predict.py train_dir=/data/0Work/my_training
+    deepaneseg-predict train_dir=/data/0Work/my_training
 ```
 GPU runs need the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/) on the
 host. `--shm-size` gives the data-loading workers enough shared memory, and `--user $(id -u):$(id -g)` creates the

From 330091578b4aa50468a0142439b79ec52d2bf7b1 Mon Sep 17 00:00:00 2001
From: Youssef Assis <ysf.assis@gmail.com>
Date: Tue, 29 Sep 2026 20:49:31 +0200
Subject: [PATCH 5/7] [FEAT] Add a command to convert datasets with aneurysm
 masks

deepaneseg-prepare converts a dataset of scans and aneurysm label
masks, by default in the ADAM challenge layout, to the P????/F.csv/
config.json layout the pipeline reads. Scans and masks are found
with {case} patterns, each mask component becomes a two-point
diameter annotation (the sphere fit used by the evaluation code), and
cases.csv maps patients back to the original cases. Cases with an
empty mask become patients without aneurysm.
---
 deepaneseg/cli/prepare.py       | 57 +++++++++++++++++++++++++++++++++
 deepaneseg/configs/prepare.yaml | 11 +++++++
 deepaneseg/data/prepare.py      | 42 ++++++++++++++++++++++++
 pyproject.toml                  |  1 +
 tests/test_cli.py               |  1 +
 5 files changed, 112 insertions(+)
 create mode 100644 deepaneseg/cli/prepare.py
 create mode 100644 deepaneseg/configs/prepare.yaml
 create mode 100644 deepaneseg/data/prepare.py

diff --git a/deepaneseg/cli/prepare.py b/deepaneseg/cli/prepare.py
new file mode 100644
index 0000000..60a6484
--- /dev/null
+++ b/deepaneseg/cli/prepare.py
@@ -0,0 +1,57 @@
+"""Convert a dataset of scans and aneurysm masks to the pipeline's layout; configuration in deepaneseg/configs/prepare.yaml.
+
+Example (ADAM challenge data): deepaneseg-prepare source_dir=/data/ADAM data_dir=/data/converted labels=[1]
+Writes one P???? folder per case (volume, F.csv with the aneurysms, config.json) and cases.csv, which maps the
+patients to the original case names.
+"""
+
+import json
+import os
+import shutil
+import sys
+
+import hydra
+import pandas as pd
+
+import deepaneseg.data.io as dio
+from deepaneseg.data.prepare import aneurysm_points, find_cases
+from deepaneseg.utils import get_logger
+
+
+@hydra.main(version_base="1.3", config_path="../configs", config_name="prepare")
+def main(cfg):
+    logger = get_logger("Prepare")
+    cases = find_cases(cfg.source_dir, cfg.scan)
+    if not cases:
+        sys.exit(f"No scan matches '{cfg.scan}' in {cfg.source_dir}")
+    masks = {case: os.path.join(cfg.source_dir, cfg.mask.replace("{case}", case)) for case in cases} if cfg.mask else {}
+    missing = sorted(case for case, path in masks.items() if not os.path.isfile(path))
+    if missing:
+        raise FileNotFoundError(f"No aneurysm mask '{cfg.mask}' for {', '.join(missing)}")
+    if dio.fetch_patient_dirs(cfg.data_dir) and not cfg.overwrite:
+        sys.exit(f"{cfg.data_dir} already contains patients; pass overwrite=true to replace them")
+
+    rows = []
+    for i, (case, scan) in enumerate(cases.items(), start=1):
+        patient = os.path.join(cfg.data_dir, f"P{i:04d}")
+        os.makedirs(patient, exist_ok=True)
+        volume = "volume.nii.gz" if scan.endswith(".gz") else "volume.nii"
+        shutil.copyfile(scan, os.path.join(patient, volume))
+        config = {"init volume": volume}
+        points = []
+        if case in masks:
+            mask, affine = dio.read_nii_from_file(masks[case])
+            points = aneurysm_points(mask, affine, labels=cfg.labels)
+            if len(points):
+                pd.DataFrame(points, columns=["x", "y", "z"]).to_csv(os.path.join(patient, "F.csv"), index=False)
+                config["pts aneurysm"] = "F.csv"
+        with open(os.path.join(patient, "config.json"), "w") as f:
+            json.dump(config, f, indent=2)
+        rows.append({"patient": f"P{i:04d}", "case": case, "scan": scan, "aneurysms": len(points) // 2})
+        logger.info(f"{case} -> P{i:04d}: {len(points) // 2} aneurysms")
+
+    pd.DataFrame(rows).to_csv(os.path.join(cfg.data_dir, "cases.csv"), index=False)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/deepaneseg/configs/prepare.yaml b/deepaneseg/configs/prepare.yaml
new file mode 100644
index 0000000..709a7ea
--- /dev/null
+++ b/deepaneseg/configs/prepare.yaml
@@ -0,0 +1,11 @@
+# Converts a dataset of scans and aneurysm label masks (defaults: ADAM challenge layout) to the pipeline's layout
+source_dir: ???                    # dataset to convert
+data_dir: ???                      # output folder, one P???? folder per case
+scan: "{case}/orig/TOF.nii.gz"     # scan of each case, relative to source_dir; {case} matches one name
+mask: "{case}/aneurysms.nii.gz"    # aneurysm label mask of a case, relative to source_dir; null if there are none
+labels: null                       # mask values that are aneurysms, e.g. [1] for ADAM (2: treated); null: any non-zero
+overwrite: false                   # replace existing patients in data_dir
+
+hydra:
+  run:
+    dir: ${data_dir}/0Work/logs/${hydra.job.name}/${now:%Y-%m-%d_%H-%M-%S}
diff --git a/deepaneseg/data/prepare.py b/deepaneseg/data/prepare.py
new file mode 100644
index 0000000..965a8f3
--- /dev/null
+++ b/deepaneseg/data/prepare.py
@@ -0,0 +1,42 @@
+"""Conversion of a dataset of scans and aneurysm masks to the layout used by the pipeline."""
+
+import glob
+import os
+import re
+
+import numpy as np
+
+from deepaneseg.volume.selection import connected_components_to_spheres
+
+
+def find_cases(source_dir, scan_pattern):
+    """
+    Returns {case name: scan path}, sorted by case, for the files matching scan_pattern in source_dir.
+    scan_pattern is a path relative to source_dir where {case} stands for one folder or file name part,
+    e.g. "{case}/orig/TOF.nii.gz" (ADAM challenge) or "scans/{case}.nii.gz".
+    """
+    if scan_pattern.count("{case}") != 1:
+        raise ValueError(f"scan pattern must contain {{case}} exactly once, got '{scan_pattern}'")
+    before, after = scan_pattern.split("{case}")
+    regex = re.compile(re.escape(before) + r"([^/]+)" + re.escape(after) + "$")
+    cases = {}
+    for path in glob.glob(os.path.join(source_dir, scan_pattern.replace("{case}", "*"))):
+        match = regex.search(os.path.relpath(path, source_dir))
+        if match:
+            cases[match.group(1)] = path
+    return dict(sorted(cases.items()))
+
+
+def aneurysm_points(mask, affine, labels=None):
+    """
+    Annotation points of the aneurysms of a label mask: each connected component of the selected labels (any
+    non-zero value if labels is None) is approximated by a sphere, described by the two ends of a diameter along x,
+    as in the F.csv files. Returns a 2Nx3 array of points in mm.
+    """
+    selected = mask > 0 if labels is None else np.isin(mask, list(labels))
+    spheres = connected_components_to_spheres(selected, affine)
+    offset = np.zeros_like(spheres[:, :3])
+    offset[:, 0] = spheres[:, 3]
+    points = np.empty((2 * len(spheres), 3))
+    points[0::2], points[1::2] = spheres[:, :3] - offset, spheres[:, :3] + offset
+    return points
diff --git a/pyproject.toml b/pyproject.toml
index e480777..797d9ec 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -25,6 +25,7 @@ dependencies = [
 ]
 
 [project.scripts]
+deepaneseg-prepare = "deepaneseg.cli.prepare:main"
 deepaneseg-remove-skull = "deepaneseg.cli.remove_skull:main"
 deepaneseg-extract-points = "deepaneseg.cli.extract_points:main"
 deepaneseg-preprocess = "deepaneseg.cli.preprocess:main"
diff --git a/tests/test_cli.py b/tests/test_cli.py
index 62faa05..c4f2916 100644
--- a/tests/test_cli.py
+++ b/tests/test_cli.py
@@ -8,6 +8,7 @@
 import pytest
 
 COMMANDS = {
+    "deepaneseg-prepare": "prepare",
     "deepaneseg-remove-skull": "remove_skull",
     "deepaneseg-extract-points": "extract_points",
     "deepaneseg-preprocess": "preprocess",

From b1a53a84cfcd43939d2a74f87d06d346d3c4c25d Mon Sep 17 00:00:00 2001
From: Youssef Assis <ysf.assis@gmail.com>
Date: Tue, 29 Sep 2026 20:49:31 +0200
Subject: [PATCH 6/7] [TEST] Check the dataset conversion on an ADAM-style
 layout

---
 tests/test_prepare.py | 104 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 104 insertions(+)
 create mode 100644 tests/test_prepare.py

diff --git a/tests/test_prepare.py b/tests/test_prepare.py
new file mode 100644
index 0000000..fef6f0f
--- /dev/null
+++ b/tests/test_prepare.py
@@ -0,0 +1,104 @@
+import subprocess
+import sys
+
+import nibabel as ni
+import numpy as np
+import pandas as pd
+import pytest
+
+from deepaneseg.data.io import fetch_patient_dirs, read_aneurysm_spheres, read_patient_data_base
+from deepaneseg.data.prepare import aneurysm_points, find_cases
+from deepaneseg.volume.selection import connected_components_to_spheres
+
+AFFINE = np.diag([0.4, 0.4, 0.6, 1.0])
+AFFINE[:3, 3] = [-50, -60, 10]
+
+
+def write_adam_case(source, name, blobs=()):
+    """ADAM challenge layout: <case>/orig/TOF.nii.gz and <case>/aneurysms.nii.gz (1: untreated, 2: treated)."""
+    (source / name / "orig").mkdir(parents=True)
+    ni.save(
+        ni.Nifti1Image(np.random.default_rng(0).random((40, 40, 30)).astype(np.float32), AFFINE),
+        source / name / "orig" / "TOF.nii.gz",
+    )
+    mask = np.zeros((40, 40, 30), dtype=np.uint8)
+    for label, (x, y, z) in blobs:
+        mask[x - 2 : x + 3, y - 2 : y + 3, z - 1 : z + 2] = label
+    ni.save(ni.Nifti1Image(mask, AFFINE), source / name / "aneurysms.nii.gz")
+    return mask
+
+
+def test_find_cases_matches_the_pattern(tmp_path):
+    for name in ("10078F", "10001", "10042B"):
+        write_adam_case(tmp_path, name)
+    (tmp_path / "notes").mkdir()  # no scan inside: ignored
+
+    cases = find_cases(str(tmp_path), "{case}/orig/TOF.nii.gz")
+
+    assert list(cases) == ["10001", "10042B", "10078F"]
+    assert cases["10001"] == str(tmp_path / "10001" / "orig" / "TOF.nii.gz")
+
+
+def test_find_cases_needs_a_case_placeholder(tmp_path):
+    with pytest.raises(ValueError, match="{case}"):
+        find_cases(str(tmp_path), "*/orig/TOF.nii.gz")
+
+
+def test_aneurysm_points_describe_each_selected_component(tmp_path):
+    mask = write_adam_case(tmp_path, "c", [(1, (10, 10, 10)), (1, (30, 30, 20)), (2, (10, 30, 15))])
+
+    points = aneurysm_points(mask, AFFINE, labels=[1])
+
+    assert points.shape == (4, 3)  # two points per untreated aneurysm
+    spheres = np.hstack(
+        [(points[::2] + points[1::2]) / 2, np.linalg.norm(points[::2] - points[1::2], axis=1, keepdims=True) / 2]
+    )
+    np.testing.assert_allclose(spheres, connected_components_to_spheres(mask == 1, AFFINE))
+    assert len(aneurysm_points(mask, AFFINE, labels=None)) == 6  # any non-zero value
+
+
+def run_prepare(source, data_dir, *args):
+    return subprocess.run(
+        [sys.executable, "-m", "deepaneseg.cli.prepare", f"source_dir={source}", f"data_dir={data_dir}", *args],
+        capture_output=True,
+        text=True,
+    )
+
+
+def test_prepare_converts_an_adam_style_dataset(tmp_path):
+    source, data_dir = tmp_path / "adam", tmp_path / "data"
+    mask = write_adam_case(source, "10002", [(1, (15, 15, 12)), (2, (30, 30, 20))])
+    write_adam_case(source, "10001")  # no aneurysm
+
+    result = run_prepare(source, data_dir, "labels=[1]")
+
+    assert result.returncode == 0, result.stderr[-2000:]
+    patients = fetch_patient_dirs(str(data_dir))
+    assert [p.split("/")[-1] for p in patients] == ["P0001", "P0002"]
+    cases = pd.read_csv(data_dir / "cases.csv")
+    assert cases[["patient", "case", "aneurysms"]].values.tolist() == [["P0001", 10001, 0], ["P0002", 10002, 1]]
+    assert read_aneurysm_spheres(patients[0]).shape == (0, 4)
+    np.testing.assert_allclose(read_aneurysm_spheres(patients[1]), connected_components_to_spheres(mask == 1, AFFINE))
+    patient = read_patient_data_base([patients[1]])[0]
+    np.testing.assert_allclose(patient["affine"], AFFINE)
+    assert patient["data"].shape == (40, 40, 30)
+
+
+def test_prepare_refuses_to_overwrite_patients(tmp_path):
+    source, data_dir = tmp_path / "adam", tmp_path / "data"
+    write_adam_case(source, "10001")
+    run_prepare(source, data_dir)
+
+    result = run_prepare(source, data_dir)
+
+    assert result.returncode != 0 and "overwrite=true" in result.stderr
+
+
+def test_prepare_reports_missing_masks(tmp_path):
+    source = tmp_path / "adam"
+    write_adam_case(source, "10001")
+    (source / "10001" / "aneurysms.nii.gz").unlink()
+
+    result = run_prepare(source, tmp_path / "data")
+
+    assert result.returncode != 0 and "10001" in result.stderr

From 7c4354b69367e5455039704e097036aa8a2e1c99 Mon Sep 17 00:00:00 2001
From: Youssef Assis <ysf.assis@gmail.com>
Date: Tue, 29 Sep 2026 20:49:40 +0200
Subject: [PATCH 7/7] [DOCS] Describe converting a dataset with
 deepaneseg-prepare

---
 README.md | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/README.md b/README.md
index b7b0fd2..98a9b39 100644
--- a/README.md
+++ b/README.md
@@ -66,6 +66,15 @@ To add an alternative (for example another scheduler), add a file to the group,
 Run the commands with `uv run` from the repository folder, or directly (`deepaneseg-train …`) from any folder once
 `.venv` is activated or the package is installed with pip.
 
+**0. Dataset conversion:** a dataset of scans with aneurysm label masks can be converted to the layout above. The
+defaults match the ADAM challenge (`<case>/orig/TOF.nii.gz`, `<case>/aneurysms.nii.gz`, where 2 marks treated
+aneurysms); other datasets only need their own `scan` and `mask` patterns. Each aneurysm becomes a two-point
+annotation, and `Data_dir/cases.csv` maps the patients to the original cases.
+```
+uv run deepaneseg-prepare source_dir=ADAM data_dir=Data_dir labels=[1]
+uv run deepaneseg-prepare source_dir=my_data data_dir=Data_dir scan='scans/{case}.nii.gz' mask='masks/{case}.nii.gz'
+```
+
 **1. Data preparation:** skull-strip the volumes, then select the negative patch centers.
 ```
 uv run deepaneseg-remove-skull data_dir=Data_dir