Merge pull request #5 from andreped/readme-refactoring

README.md

@@ -1,12 +1,57 @@
-# DeepDeformationMapRegistration
-Deep learning powered image to image registration, based on VoxelMorph
+<div align="center">
+    <img src="https://user-images.githubusercontent.com/29090665/197514781-11c92d1e-a692-4c78-8f0f-6cb863c6f572.png" alt="drawing" width="600">
+</div>
 
-# Usage
-The core functionality reside in DeepDeformationMapRegistration package, e.g., the augmentation layers, metrics, etc.
+<div align="center">
+<h1 align="center">DDMR: Deep Deformation Map Registration</h1>
+<h3 align="center">Cross-modal transfer learning and adaptive multi-task learning for improved abdominal CT registration</h3>
+ 
+**DDMR** was developed by SINTEF Health Research. A paper is submitted to X and the preprint is openly available at [arXiv](some-url-here.com).
+ 
+</div>
+
+## 💻 Getting started
+
+1. Setup virtual environment:
+```
+virtualenv -ppython3 venv --clear
+source venv/bin/activate
+```
+
+2. Install requirements:
+```
+pip install -r requirements.txt
+```
+
+## 🏋️‍♂️ Training
 
 Use the "MultiTrain" scripts to launch the trainings, providing the neccesary parameters. Those in the COMET folder accepts a .ini configuration file (see COMET/train_config_files for example configurations).
 
+For instance:
+```
+python TrainingScripts/Train_3d.py
+```
+
+## 🔍 Evaluate
+
 Use Evaluate_network to test the trained models. On the Brain folder, use "Evaluate_network__test_fixed.py" instead.
 
-# Warning
-The code in this repo is in need of a thorough clean and refactoring. So expect the unexpected.
+For instance:
+```
+python EvaluationScripts/evaluation.py
+```
+
+## 🏆 Acknowledgements
+
+## ✨ How to cite
+Please, consider citing our paper, if you find the work useful:
+<pre>
+  @misc{frutos2022ddmr,
+  author={Pérez de Frutos, Javier and Pedersen, André and Pelanis, Egidijus and Bouget, David and Survarachakan, Shanmugapriya and Langø, Thomas and Lindseth, Frank and Elle, Ole-Jakob},
+  publisher = {arXiv},
+  title={Cross-modal transfer learning and adaptive multi-task learning for improved abdominal CT registration}, 
+  year={2022},
+  doi = {10.48550/ARXIV.2011.06033},
+  url = {https://arxiv.org/abs/2011.06033}}
+</pre>
+