--- a/README.md
+++ b/README.md
@@ -1,172 +1,172 @@
-# Hippocampus Segmentation from MRI using V-Net
-In this repo, hippocampus segmentation from MRI is performed 
-using a Convolutional Neural Network (CNN) architecture based on
-[V-Net](https://arxiv.org/abs/1606.04797).
-The dataset is publicly available from the 
-[Medical Segmentation Decathlon Challenge](http://medicaldecathlon.com/),
-and can be downloaded from 
-[here](https://drive.google.com/drive/folders/1HqEgzS8BV2c7xYNrZdEAnrHk7osJJ--2).
-
-The [PyTorch](https://pytorch.org/) library has been used to write the model architecture 
-and performing the training and validation. 
-[SimpleITK](https://simpleitk.org/) has been exploited to handle I/O of medical images. 
-3D Data Augmentation has been made by employing [torchio](https://arxiv.org/abs/2003.04696).
-
-A 5-folders cross validation has been performed on the training set, yielding a 
-Mean Multi Dice Coefficient of 0.8727 +/- 0.0364, a Dice Coefficient for 
-Anterior Hippocampus of 0.8821 +/- 0.0363 and a Dice Coefficient for 
-Posterior Hippocampus of 0.8634 +/- 0.0415.
-The results are reported as "mean +/- std". 
-
-Meshes and images reported in the ```images``` folder have been obtained exploiting 
-[ITK-SNAP](http://www.itksnap.org/).
-
-### Quality Measures
-<table>
-<tr>
-<th colspan="4">Results</th>
-</tr>
-<tr>
-<th> Model </th>
-<th> Mean Dice per case </th>
-<th> Dice per case (Anterior) </th>
-<th> Dice per case (Posterior) </th>
-</tr>
-<tr>
-<td>3D V-Net (no data augmentation)</td>
-<td>0.8727 +/- 0.0364</td>
-<td>0.8821 +/- 0.0363</td>
-<td>0.8634 +/- 0.0415</td>
-</tr>
-<tr>
-<td>3D V-Net (with data augmentation)</td>
-<td> 0.8761 +/- 0.0374 </td>
-<td> 0.8875 +/- 0.0354 </td>
-<td> 0.8647 +/- 0.0455 </td>
-</tr>
-</table>
-
-### Confusion Matrices
-#### No Data Augmentation
-<table>
-<tr>
-<th>Confusion Matrix </th>
-<th>Normalized Confusion Matrix</th>
-</tr>
-<tr>
-<th>
-<img src="images/conf_matrix_no_norm_no_augm_torchio.png" alt="Confusion Matrix (Cross-validation)" width="400"/>
-</th>
-<th>
-<img src="images/conf_matrix_normalized_row_no_augm_torchio.png" alt="Normalized Confusion Matrix (Cross-validation)" width="400"/>
-</th>
-<tr>
-</table>
-
-#### With Data Augmentation
-<table>
-<tr>
-<th>Confusion Matrix </th>
-<th>Normalized Confusion Matrix</th>
-</tr>
-<tr>
-<th>
-<img src="images/conf_matrix_no_norm_augm.png" alt="Confusion Matrix (Cross-validation)" width="400"/>
-</th>
-<th>
-<img src="images/conf_matrix_normalized_row_augm.png" alt="Normalized Confusion Matrix (Cross-validation)" width="400"/>
-</th>
-<tr>
-</table>
-
-
-### TODO List
-- [x] Automatic Download of dataset
-- [x] CNN Architecture Definition
-- [x] 3D Data Loader for Nifti files
-- [x] Definition of loss functions
-- [x] Training loop
-- [x] Cross-validation on Train set
-- [x] Command Line Interface for training 
-- [x] Command Line Interface for validation 
-- [x] 3D Data Augmentation 
-- [ ] Tuning of Optimal Parameters for 3D Data Augmentation
-- [ ] Validation on Test set
-
-## Usage
-Use ```python setup.py install``` for installing this package.
-A complete run (dataset download, train, validation) of the package may be the following:
-```console
-git clone https://github.com/Nicolik/HippocampusSegmentationMRI.git
-cd HippocampusSegmentationMRI
-python setup.py install
-python run/download.py
-python run/train.py 
-python run/validate.py
-```
-### Dataset
-If you want to download the original dataset, run ```run/download.py```.
-The syntax is as follows:
-```console
-python run/download.py --dir=path/to/dataset/dir
-```
-### Training
-If you simply want to perform the training, run ```run/train.py```.
-The syntax is as follows:
-```console
-python run/train.py --epochs=NUM_EPOCHS --batch=BATCH_SIZE --workers=NUM_WORKERS --lr=LR
-```
-If you want to edit the configuration, you can also modify the ```config/config.py``` file. 
-In particular, consider the class ```SemSegMRIConfig```. 
-If you want to play with data augmentation (built with ```torchio```), 
-modify the ```config/augm.py``` file.
-
-### Validation
-If you want to perform the cross-validation, run ```run/validate.py``` or ```run/validate_torchio.py```.
-The syntax is as follows:
-```console
-python run/validate.py --dir=path/to/logs/dir --write=WRITE --verbose=VERBOSE
-```
-```console
-python run/validate_torchio.py --dir=path/to/logs/dir --verbose=VERBOSE
-```
-The former adopts a loop from scratch, whereas the latter exploits the DataLoader created upon ```torchio```. 
-
-## Output Results
-### Sample Images (Training set)
-#### Ground Truth Images
-<table>
-<tr>
-<th>Ground Truth - MRI 327 (1)</th>
-<th>Ground Truth - MRI 327 (2)</th>
-</tr>
-<tr>
-<td><img src="images/327_gt_01.png" alt="Ground Truth - MRI 327 (1)" width="250"/></td>
-<td><img src="images/327_gt_02.png" alt="Ground Truth - MRI 327 (2)" width="250"/></td>
-</tr>
-</table>
-
-#### Predicted Images
-<table>
-<tr>
-<th>Prediction   - MRI 327 (1)</th>
-<th>Prediction   - MRI 327 (2)</th>
-</tr>
-<tr>
-<td><img src="images/327_pred_01.png" alt="Prediction   - MRI 327 (1)" width="250"/></td>
-<td><img src="images/327_pred_02.png" alt="Prediction   - MRI 327 (2)" width="250"/></td>
-</tr>
-</table>
-
-### Sample Images (Test set)
-#### Predicted Images
-<table>
-<tr>
-<th>Prediction   - MRI 283 (1)</th>
-<th>Prediction   - MRI 283 (2)</th>
-</tr>
-<tr>
-<td><img src="images/283_pred_01.png" alt="Prediction   - MRI 283 (1)" width="250"/></td>
-<td><img src="images/283_pred_02.png" alt="Prediction   - MRI 283 (2)" width="250"/></td>
-</tr>
+# Hippocampus Segmentation from MRI using V-Net
+In this repo, hippocampus segmentation from MRI is performed 
+using a Convolutional Neural Network (CNN) architecture based on
+[V-Net](https://arxiv.org/abs/1606.04797).
+The dataset is publicly available from the 
+[Medical Segmentation Decathlon Challenge](http://medicaldecathlon.com/),
+and can be downloaded from 
+[here](https://drive.google.com/drive/folders/1HqEgzS8BV2c7xYNrZdEAnrHk7osJJ--2).
+
+The [PyTorch](https://pytorch.org/) library has been used to write the model architecture 
+and performing the training and validation. 
+[SimpleITK](https://simpleitk.org/) has been exploited to handle I/O of medical images. 
+3D Data Augmentation has been made by employing [torchio](https://arxiv.org/abs/2003.04696).
+
+A 5-folders cross validation has been performed on the training set, yielding a 
+Mean Multi Dice Coefficient of 0.8727 +/- 0.0364, a Dice Coefficient for 
+Anterior Hippocampus of 0.8821 +/- 0.0363 and a Dice Coefficient for 
+Posterior Hippocampus of 0.8634 +/- 0.0415.
+The results are reported as "mean +/- std". 
+
+Meshes and images reported in the ```images``` folder have been obtained exploiting 
+[ITK-SNAP](http://www.itksnap.org/).
+
+### Quality Measures
+<table>
+<tr>
+<th colspan="4">Results</th>
+</tr>
+<tr>
+<th> Model </th>
+<th> Mean Dice per case </th>
+<th> Dice per case (Anterior) </th>
+<th> Dice per case (Posterior) </th>
+</tr>
+<tr>
+<td>3D V-Net (no data augmentation)</td>
+<td>0.8727 +/- 0.0364</td>
+<td>0.8821 +/- 0.0363</td>
+<td>0.8634 +/- 0.0415</td>
+</tr>
+<tr>
+<td>3D V-Net (with data augmentation)</td>
+<td> 0.8761 +/- 0.0374 </td>
+<td> 0.8875 +/- 0.0354 </td>
+<td> 0.8647 +/- 0.0455 </td>
+</tr>
+</table>
+
+### Confusion Matrices
+#### No Data Augmentation
+<table>
+<tr>
+<th>Confusion Matrix </th>
+<th>Normalized Confusion Matrix</th>
+</tr>
+<tr>
+<th>
+<img src="https://github.com/Nicolik/HippocampusSegmentationMRI/blob/torchio/images/conf_matrix_no_norm_no_augm_torchio.png?raw=true" alt="Confusion Matrix (Cross-validation)" width="400"/>
+</th>
+<th>
+<img src="https://github.com/Nicolik/HippocampusSegmentationMRI/blob/torchio/images/conf_matrix_normalized_row_no_augm_torchio.png?raw=true" alt="Normalized Confusion Matrix (Cross-validation)" width="400"/>
+</th>
+<tr>
+</table>
+
+#### With Data Augmentation
+<table>
+<tr>
+<th>Confusion Matrix </th>
+<th>Normalized Confusion Matrix</th>
+</tr>
+<tr>
+<th>
+<img src="https://github.com/Nicolik/HippocampusSegmentationMRI/blob/torchioimages/conf_matrix_no_norm_augm.png?raw=true" alt="Confusion Matrix (Cross-validation)" width="400"/>
+</th>
+<th>
+<img src="https://github.com/Nicolik/HippocampusSegmentationMRI/blob/torchio/images/conf_matrix_normalized_row_augm.png?raw=true" alt="Normalized Confusion Matrix (Cross-validation)" width="400"/>
+</th>
+<tr>
+</table>
+
+
+### TODO List
+- [x] Automatic Download of dataset
+- [x] CNN Architecture Definition
+- [x] 3D Data Loader for Nifti files
+- [x] Definition of loss functions
+- [x] Training loop
+- [x] Cross-validation on Train set
+- [x] Command Line Interface for training 
+- [x] Command Line Interface for validation 
+- [x] 3D Data Augmentation 
+- [ ] Tuning of Optimal Parameters for 3D Data Augmentation
+- [ ] Validation on Test set
+
+## Usage
+Use ```python setup.py install``` for installing this package.
+A complete run (dataset download, train, validation) of the package may be the following:
+```console
+git clone https://github.com/Nicolik/HippocampusSegmentationMRI.git
+cd HippocampusSegmentationMRI
+python setup.py install
+python run/download.py
+python run/train.py 
+python run/validate.py
+```
+### Dataset
+If you want to download the original dataset, run ```run/download.py```.
+The syntax is as follows:
+```console
+python run/download.py --dir=path/to/dataset/dir
+```
+### Training
+If you simply want to perform the training, run ```run/train.py```.
+The syntax is as follows:
+```console
+python run/train.py --epochs=NUM_EPOCHS --batch=BATCH_SIZE --workers=NUM_WORKERS --lr=LR
+```
+If you want to edit the configuration, you can also modify the ```config/config.py``` file. 
+In particular, consider the class ```SemSegMRIConfig```. 
+If you want to play with data augmentation (built with ```torchio```), 
+modify the ```config/augm.py``` file.
+
+### Validation
+If you want to perform the cross-validation, run ```run/validate.py``` or ```run/validate_torchio.py```.
+The syntax is as follows:
+```console
+python run/validate.py --dir=path/to/logs/dir --write=WRITE --verbose=VERBOSE
+```
+```console
+python run/validate_torchio.py --dir=path/to/logs/dir --verbose=VERBOSE
+```
+The former adopts a loop from scratch, whereas the latter exploits the DataLoader created upon ```torchio```. 
+
+## Output Results
+### Sample Images (Training set)
+#### Ground Truth Images
+<table>
+<tr>
+<th>Ground Truth - MRI 327 (1)</th>
+<th>Ground Truth - MRI 327 (2)</th>
+</tr>
+<tr>
+<td><img src="https://github.com/Nicolik/HippocampusSegmentationMRI/blob/torchio/images/327_gt_01.png?raw=true" alt="Ground Truth - MRI 327 (1)" width="250"/></td>
+<td><img src="https://github.com/Nicolik/HippocampusSegmentationMRI/blob/torchio/images/327_gt_02.png?raw=true" alt="Ground Truth - MRI 327 (2)" width="250"/></td>
+</tr>
+</table>
+
+#### Predicted Images
+<table>
+<tr>
+<th>Prediction   - MRI 327 (1)</th>
+<th>Prediction   - MRI 327 (2)</th>
+</tr>
+<tr>
+<td><img src="https://github.com/Nicolik/HippocampusSegmentationMRI/blob/torchioimages/327_pred_01.png?raw=true" alt="Prediction   - MRI 327 (1)" width="250"/></td>
+<td><img src="https://github.com/Nicolik/HippocampusSegmentationMRI/blob/torchio/images/327_pred_02.png?raw=true" alt="Prediction   - MRI 327 (2)" width="250"/></td>
+</tr>
+</table>
+
+### Sample Images (Test set)
+#### Predicted Images
+<table>
+<tr>
+<th>Prediction   - MRI 283 (1)</th>
+<th>Prediction   - MRI 283 (2)</th>
+</tr>
+<tr>
+<td><img src="https://github.com/Nicolik/HippocampusSegmentationMRI/blob/torchio/images/283_pred_01.png?raw=true" alt="Prediction   - MRI 283 (1)" width="250"/></td>
+<td><img src="https://github.com/Nicolik/HippocampusSegmentationMRI/blob/torchio/images/283_pred_02.png?raw=true" alt="Prediction   - MRI 283 (2)" width="250"/></td>
+</tr>
 </table>
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