--- a/README.md
+++ b/README.md
@@ -1,66 +1,66 @@
-# Brain segmentation
-
-This is a source code for the deep learning segmentation used in the paper [Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm.](https://doi.org/10.1016/j.compbiomed.2019.05.002)
-It employs a U-Net like network for skull stripping and FLAIR abnormality segmentation.
-This repository contains a set of functions for data preprocessing (MatLab), training and inference (Python).
-Weights for trained models are provided and can be used for deep learning based skull stripping or fine-tuning on a different dataset.
-If you use our model or weights, please cite:
-
-```
-@article{buda2019association,
-  title={Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm},
-  author={Buda, Mateusz and Saha, Ashirbani and Mazurowski, Maciej A},
-  journal={Computers in Biology and Medicine},
-  volume={109},
-  year={2019},
-  publisher={Elsevier},
-  doi={10.1016/j.compbiomed.2019.05.002}
-}
-```
-
-Developed by [mateuszbuda](https://github.com/mateuszbuda).
-
-The repository is divided into two folders.
-One for skull stripping and one for FLAIR abnormality segmentation.
-They are based on the same model architecture but can be used separately.
-
-## Prerequisites
-
-- MatLab 2016b for pre-processing
-- Python 2 with dependencies listed in the `requirements.txt` file
-```
-sudo pip install -r requirements.txt
-```
-
-## Results
-
-Below we show qualitative results for the average and median case.
-Blue outline corresponds to ground truth and red to the final automatic segmentation output.
-Images show FLAIR modality after preprocessing and skull stripping.
-
-| Average Case | Median Case |
-|:----------:|:---------:|
-|![Average case](CS_6669.gif)|![Median case](HT_7473.gif)|
-
-The distribution of Dice similarity coefficient (DSC) for the whole dataset of 110 cases used in our study.
-
-![DSC distribution](DSC_distribution.png)
-
-The red vertical line corresponds to mean DSC (83.60%) and the green one to median DSC (87.33%).
-
-## Trained weights
-
-To download trained weights use `download_weights.sh` script located in both skull stripping or flair segmentation folder.
-It downloads *.h5 file with weights corresponding to training log shown in each task specific folder and responsible for the results reported there.
-
-## U-Net architecture
-
-The figure below shows a U-Net architecture implemented in this repository.
-
-![unet](unet.png)
-
-## Data
-
-![brain-mri-lgg](brain-mri-lgg.png)
-
-[kaggle.com/mateuszbuda/lgg-mri-segmentation](https://www.kaggle.com/mateuszbuda/lgg-mri-segmentation)
+# Brain segmentation
+
+This is a source code for the deep learning segmentation used in the paper [Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm.](https://doi.org/10.1016/j.compbiomed.2019.05.002)
+It employs a U-Net like network for skull stripping and FLAIR abnormality segmentation.
+This repository contains a set of functions for data preprocessing (MatLab), training and inference (Python).
+Weights for trained models are provided and can be used for deep learning based skull stripping or fine-tuning on a different dataset.
+If you use our model or weights, please cite:
+
+```
+@article{buda2019association,
+  title={Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm},
+  author={Buda, Mateusz and Saha, Ashirbani and Mazurowski, Maciej A},
+  journal={Computers in Biology and Medicine},
+  volume={109},
+  year={2019},
+  publisher={Elsevier},
+  doi={10.1016/j.compbiomed.2019.05.002}
+}
+```
+
+Developed by [mateuszbuda](https://github.com/mateuszbuda).
+
+The repository is divided into two folders.
+One for skull stripping and one for FLAIR abnormality segmentation.
+They are based on the same model architecture but can be used separately.
+
+## Prerequisites
+
+- MatLab 2016b for pre-processing
+- Python 2 with dependencies listed in the `requirements.txt` file
+```
+sudo pip install -r requirements.txt
+```
+
+## Results
+
+Below we show qualitative results for the average and median case.
+Blue outline corresponds to ground truth and red to the final automatic segmentation output.
+Images show FLAIR modality after preprocessing and skull stripping.
+
+| Average Case | Median Case |
+|:----------:|:---------:|
+|![Average case](CS_6669.gif)|![Median case](HT_7473.gif)|
+
+The distribution of Dice similarity coefficient (DSC) for the whole dataset of 110 cases used in our study.
+
+![DSC distribution](https://github.com/MaciejMazurowski/brain-segmentation/blob/master/DSC_distribution.png?raw=true)
+
+The red vertical line corresponds to mean DSC (83.60%) and the green one to median DSC (87.33%).
+
+## Trained weights
+
+To download trained weights use `download_weights.sh` script located in both skull stripping or flair segmentation folder.
+It downloads *.h5 file with weights corresponding to training log shown in each task specific folder and responsible for the results reported there.
+
+## U-Net architecture
+
+The figure below shows a U-Net architecture implemented in this repository.
+
+![unet](unet.png)
+
+## Data
+
+![brain-mri-lgg](https://github.com/MaciejMazurowski/brain-segmentation/blob/master/brain-mri-lgg.png?raw=true)
+
+[kaggle.com/mateuszbuda/lgg-mri-segmentation](https://www.kaggle.com/mateuszbuda/lgg-mri-segmentation)