Merge branch 'master' of https://github.com/andreped/livermask into master

README.md

@@ -1,41 +1,25 @@
 # Automatic liver segmentation in CT using deep learning
 [![license](https://img.shields.io/github/license/DAVFoundation/captain-n3m0.svg?style=flat-square)](https://github.com/DAVFoundation/captain-n3m0/blob/master/LICENSE)
 
-#### Trained U-Net on the LITS dataset is automatically downloaded when running the inference script and can be used as you wish, ENJOY! :)
+#### Pretrained U-Net model is automatically downloaded when running the inference script and can be used as you wish, ENJOY! :)
 
 <img src="figures/Segmentation_CustusX.PNG" width="70%" height="70%">
 
-The figure shows a predicted liver mask with the corresponding patient CT in 3DSlicer. It is the Volume-10 from the LITS17 dataset.
+## Install
 
-### Credit
-The LITS dataset can be accessible from [here](https://competitions.codalab.org), and the corresponding paper for the challenge from [here](https://arxiv.org/abs/1901.04056). If trained model is used, please consider citing this paper.
-
-### Usage:
-
-1) Clone repo:
-```
-git clone https://github.com/andreped/livermask.git
-cd livermask
-```
-2) Create virtual environment and intall dependencies:
-```
-virtualenv -ppython3 venv
-source venv/bin/activate
-pip install -r /path/to/requirements.txt
 ```
-3) Run livermask method:
-```
-cd livermask
-python livermask.py "path_to_ct_nifti.nii" "output_name.nii"
+pip install git+https://github.com/andreped/livermask.git
 ```
 
-If you lack any modules after, try installing them through setup.py (could be done instead of using requirements.txt):
+## Usage:
+
 ```
-pip install wheel
-python setup.py bdist_wheel
+livermask --input path-to-nifti.nii --output path-to-output-file.nii
 ```
 
-### DICOM/NIfTI format
+In addition, there is the optional `--cpu` action to disable the GPU (force computations on CPU only) if necessary.
+
+## DICOM/NIfTI format
 Pipeline assumes input is in the NIfTI format, and output a binary volume in the same format (.nii).
 DICOM can be converted to NIfTI using the CLI [dcm2niix](https://github.com/rordenlab/dcm2niix), as such:
 ```
@@ -44,9 +28,14 @@ dcm2niix -s y -m y -d 1 "path_to_CT_folder" "output_name"
 
 Note that "-d 1" assumed that "path_to_CT_folder" is the folder just before the set of DICOM scans you want to import and convert. This can be removed if you want to convert multiple ones at the same time. It is possible to set "." for "output_name", which in theory should output a file with the same name as the DICOM folder, but that doesn't seem to happen...
 
-### Troubleshooting
+## Troubleshooting
 You might have issues downloading the model when using VPN. If any issues are observed, try to disable VPN and try again.
 
+## Acknowledgements
+The model was trained on the LITS dataset. The dataset is openly accessible and can be downloaded from [here](https://competitions.codalab.org). If this tool is used, please, consider citing the corresponding [LITS challenge dataset paper](https://arxiv.org/abs/1901.04056).
+
+Disclaimer, I have no affiliation with the LITS challenge, the LITS dataset, or the challenge paper. I only wish to provide an open, free-to-use tool that people may find useful :)
+
 ------
 
 Made with :heart: and python