--- a/README.md
+++ b/README.md
@@ -1,56 +1,56 @@
-# SAML & A Multi-site Dataset for Prostate MRI Segmentation
-by [Quande Liu](https://github.com/liuquande), [Qi Dou](http://www.cse.cuhk.edu.hk/~qdou/), [Pheng-Ann Heng](http://www.cse.cuhk.edu.hk/~pheng/). 
-
-### Introduction
-
-* The Tensorflow implementation for our MICCAI 2020 paper '[Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains](https://arxiv.org/pdf/2007.02035.pdf)'. 
-
-<p align="center">
-  <img src="figure/saml.png"  width="650"/>
-</p>
-
-* A well-organized multi-site dataset (from six data sources) for prostate MRI segmentation, that can support research in various problem settings with need of multi-site data, such as Domain Generalization, Multi-site Learning and Life-long Learning, etc. For more details and downloading link of the dataset, please [Find Here](https://liuquande.github.io/SAML/).
-    
-
-<p align="center">
-  <img src="figure/protocol.png"  width="650"/>
-</p>
-  
-
-### Setup & Usage for the Code
-
-1. Check dependencies:
-   ```shell
-   python==2.7.17
-   numpy==1.16.6
-   scipy==1.2.1
-   tensorflow-gpu==1.12.0
-   tensorboard==1.12.2
-   SimpleITK==1.2.0
-   ```
-2. To train the model, you need to specify the training configurations (can simply use the default setting) in main.py, then run:
-   ```shell
-   python main.py --phase=train
-   ```
-
-2. To evaluate the model, run:
-   ```shell
-   python main.py --phase=test --restore_model='/path/to/test_model.cpkt'
-   ```
-   You will see the output results in the folder `./output/`.
-
-### Citation
-If this repository is useful for your research, please cite:
-
-```
-@article{liu2020shape,
-  title={Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains},
-  author={Liu, Quande and Dou, Qi and Heng, Pheng-Ann},
-  journal={International Conference on Medical Image Computing and Computer Assisted Intervention},
-  year={2020}
-}
-```
-
-### Questions
-
-For further question about the code or dataset, please contact 'qdliu@cse.cuhk.edu.hk'
+# SAML & A Multi-site Dataset for Prostate MRI Segmentation
+by [Quande Liu](https://github.com/liuquande), [Qi Dou](http://www.cse.cuhk.edu.hk/~qdou/), [Pheng-Ann Heng](http://www.cse.cuhk.edu.hk/~pheng/). 
+
+### Introduction
+
+* The Tensorflow implementation for our MICCAI 2020 paper '[Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains](https://arxiv.org/pdf/2007.02035.pdf)'. 
+
+<p align="center">
+  <img src="https://github.com/liuquande/SAML/blob/master/figure/saml.png?raw=true"  width="650"/>
+</p>
+
+* A well-organized multi-site dataset (from six data sources) for prostate MRI segmentation, that can support research in various problem settings with need of multi-site data, such as Domain Generalization, Multi-site Learning and Life-long Learning, etc. For more details and downloading link of the dataset, please [Find Here](https://liuquande.github.io/SAML/).
+    
+
+<p align="center">
+  <img src="https://github.com/liuquande/SAML/blob/master/figure/protocol.png?raw=true"  width="650"/>
+</p>
+  
+
+### Setup & Usage for the Code
+
+1. Check dependencies:
+   ```shell
+   python==2.7.17
+   numpy==1.16.6
+   scipy==1.2.1
+   tensorflow-gpu==1.12.0
+   tensorboard==1.12.2
+   SimpleITK==1.2.0
+   ```
+2. To train the model, you need to specify the training configurations (can simply use the default setting) in main.py, then run:
+   ```shell
+   python main.py --phase=train
+   ```
+
+2. To evaluate the model, run:
+   ```shell
+   python main.py --phase=test --restore_model='/path/to/test_model.cpkt'
+   ```
+   You will see the output results in the folder `./output/`.
+
+### Citation
+If this repository is useful for your research, please cite:
+
+```
+@article{liu2020shape,
+  title={Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains},
+  author={Liu, Quande and Dou, Qi and Heng, Pheng-Ann},
+  journal={International Conference on Medical Image Computing and Computer Assisted Intervention},
+  year={2020}
+}
+```
+
+### Questions
+
+For further question about the code or dataset, please contact 'qdliu@cse.cuhk.edu.hk'