Some readme fixes

README.md

@@ -1,24 +1,23 @@
-# DLMUSE - Deep Learning MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters
+### DLMUSE - Deep Learning MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters
 
 ## Overview
 
-DLMUSE uses a trained [nnUNet](https://github.com/MIC-DKFZ/nnUNet/tree/nnunetv1) model to compute the segmentation of the brain into [MUSE](https://www.med.upenn.edu/cbica/sbia/muse.html) ROIs from the nifti image of the Intra Cranial Volume (ICV - see [DLICV method](https://github.com/CBICA/DLICV)), oriented in _**LPS**_ orientation. It produces the segmented brain, along with a .csv file of the calculated volumes of each ROI. 
+DLMUSE uses a trained [nnUNet](https://github.com/MIC-DKFZ/nnUNet/tree/nnunetv1) model to compute the segmentation of the brain into [MUSE](https://www.med.upenn.edu/cbica/sbia/muse.html) ROIs from the nifti image of the Intra Cranial Volume (ICV - see [DLICV method](https://github.com/CBICA/DLICV)), oriented in _**LPS**_ orientation. It produces the segmented brain, along with a .csv file of the calculated volumes of each ROI.
 
-## Installation
+### Installation
 
-### As a python package
+## As a python package
 
 ```bash
 pip install DLMUSE
 ```
 
-### Directly from this repository
+## Directly from this repository
 
 ```bash
 git clone https://github.com/CBICA/DLMUSE
 cd DLMUSE
-conda create -n DLMUSE -y python=3.8 && conda activate DLMUSE
-pip install .
+pip install -e .
 ```
 
 ### Using docker
@@ -31,49 +30,9 @@ docker pull aidinisg/dlmuse:0.0.1
 
 A pre-trained nnUNet model can be found in the [DLMUSE-0.0.1 release](https://github.com/CBICA/DLMUSE/releases/tag/v0.0.1) as an [artifact](https://github.com/CBICA/DLMUSE/releases/download/v0.0.1/model.zip). Feel free to use it under the package's [license](LICENSE).
 
-### Import as a python package
-
-```python
-from DLMUSE.compute_icv import compute_volume
-
-# Assuming your nifti file is named 'input.nii.gz'
-volume_image = compute_volume("input.nii.gz", "output.nii.gz", "path/to/model/")
-```
-
-### From the terminal
-
-```bash
-DLMUSE --input input.nii.gz --output output.nii.gz --model path/to/model
-```
-
-Replace the `input.nii.gz` with the path to your input nifti file, as well as the model path.
-
-Example:
-
-Assuming a file structure like so:
-
+### From command line
 ```bash
-.
-├── in
-│   ├── input1.nii.gz
-│   ├── input2.nii.gz
-│   └── input3.nii.gz
-├── model
-│   ├── fold_0
-│   ├── fold_2
-│   │   ├── debug.json
-│   │   ├── model_final_checkpoint.model
-│   │   ├── model_final_checkpoint.model.pkl
-│   │   ├── model_latest.model
-│   │   ├── model_latest.model.pkl
-│   └── plans.pkl
-└── out
-```
-
-An example command might be:
-
-```bash
-DLMUSE --input path/to/input/ --output path/to/output/ --model path/to/model/
+DLMUSE -i "image_folder" -o "path to output folder" -m "path to model weights" -f 0 -tr nnUNetTrainer -c 3d_fullres -p nnUNetPlans -d "id" -device cuda/cpu/mps
 ```
 
 ### Using the docker container
@@ -108,15 +67,6 @@ Contributions are welcome! Please refer to our [CONTRIBUTING.md](CONTRIBUTING.md
 If you're a developer looking to contribute, you'll first need to set up a development environment. After cloning the repository, you can install the development dependencies with:
 
 ```bash
-pip install -r requirements-test.txt
+pip install -r requirements.txt
 ```
-
 This will install the packages required for running tests and formatting code. Please make sure to write tests for new code and run them before submitting a pull request.
-
-### Running Tests
-
-You can run the test suite with the following command:
-
-```bash
-pytest
-```

setup.py

@@ -1,8 +1,7 @@
 """Setup tool for DLMUSE."""
 
-import io
-import os
 from pathlib import Path
+
 from setuptools import find_packages, setup
 
 this_directory = Path(__file__).parent
@@ -21,31 +20,26 @@
     download_url="https://github.com/CBICA/DLICV/",
     url="https://github.com/CBICA/DLICV/",
     packages=find_packages(exclude=["tests", ".github"]),
-    install_requires=[
-        "torch",
-        "pathlib",
-        "argparse",
-        "nnunetv2"
-    ],
+    install_requires=["torch", "pathlib", "argparse", "nnunetv2"],
     entry_points={"console_scripts": ["DLMUSE = src.__main__:main"]},
     classifiers=[
-                    "Intended Audience :: Developers",
-                    "Intended Audience :: Science/Research",
-                    "Intended Audience :: Healthcare Industry",
-                    "Programming Language :: Python :: 3",
-                    "Topic :: Scientific/Engineering :: Artificial Intelligence",
-                    "Topic :: Scientific/Engineering :: Image Processing",
-                    "Topic :: Scientific/Engineering :: Medical Science Apps.",
-                ],
+        "Intended Audience :: Developers",
+        "Intended Audience :: Science/Research",
+        "Intended Audience :: Healthcare Industry",
+        "Programming Language :: Python :: 3",
+        "Topic :: Scientific/Engineering :: Artificial Intelligence",
+        "Topic :: Scientific/Engineering :: Image Processing",
+        "Topic :: Scientific/Engineering :: Medical Science Apps.",
+    ],
     license="By installing/using DeepMRSeg, the user agrees to the following license: See https://www.med.upenn.edu/cbica/software-agreement-non-commercial.html",
-    keywords = [
-                    'deep learning',
-                    'image segmentation',
-                    'semantic segmentation',
-                    'medical image analysis',
-                    'medical image segmentation',
-                    'nnU-Net',
-                    'nnunet'
-                ],
+    keywords=[
+        "deep learning",
+        "image segmentation",
+        "semantic segmentation",
+        "medical image analysis",
+        "medical image segmentation",
+        "nnU-Net",
+        "nnunet",
+    ],
     package_data={"DLMUSE": ["VERSION"]},
 )