--- a/README.md
+++ b/README.md
@@ -1,35 +1,35 @@
-# DeepLearning-HeartSegmentation
-Heart segmentation in chest CT scans 
-
-## Introduction
-
-This project aims to develop and evaluate deep learning models for the accurate segmentation of the heart in medical images, particularly in non-contrast and non-gated computed tomography (CT) scans. The precise delineation of the heart region is crucial for various medical applications.
-
-## Models and Architectures
-
-### U-Net++
-
-We employed the U-Net++ architecture as a base model for heart segmentation. The model was trained using a Binary Cross-Entropy loss function. After 100 epochs of training, the model achieved an average Dice similarity coefficient of 0.72 and a Jaccard index of 0.67 on the validation set.
-
-### MA-Net
-
-The Multi-scale Attention Net (MA-Net) architecture was implemented to improve heart segmentation. After 100 epochs, this model outperformed U-Net++ with an average Dice coefficient of 0.85 and a Jaccard index of 0.82 on the validation set. Further training for 200 epochs reduced oscillations in the training and validation metrics.
-<p align='center'>
-<img src='https://github.com/dcrovo/DeepLearning-HeartSegmentation/blob/main/imgs/MANET_TRAIN.png' />  
-<img src='https://github.com/dcrovo/DeepLearning-HeartSegmentation/blob/main/imgs/MANET_DICE_VAL.png' />  
-
-</p>
-
-## Results
-This is a sample CT scan segmented by the MANET model in red and by a certified board radiologist in green.
-<p align='center'>
-<img src='https://github.com/dcrovo/DeepLearning-HeartSegmentation/blob/main/imgs/final.png' width=496 height=515/>  
-</p>
-
-
-In summary, the MA-Net model demonstrated superior performance in heart segmentation compared to U-Net++. It achieved a Dice coefficient of 0.883, which compares favourably with state-of-the-art models. However, both models exhibited some overfitting, likely due to the limited size of the training dataset.
-
-## Future Work
-
-To enhance the models further, future work will focus on hyperparameter optimization and increased data augmentation. The well-segmented heart regions will be used as a crucial step for addressing broader medical image analysis challenges.
-
+# DeepLearning-HeartSegmentation
+Heart segmentation in chest CT scans 
+
+## Introduction
+
+This project aims to develop and evaluate deep learning models for the accurate segmentation of the heart in medical images, particularly in non-contrast and non-gated computed tomography (CT) scans. The precise delineation of the heart region is crucial for various medical applications.
+
+## Models and Architectures
+
+### U-Net++
+
+We employed the U-Net++ architecture as a base model for heart segmentation. The model was trained using a Binary Cross-Entropy loss function. After 100 epochs of training, the model achieved an average Dice similarity coefficient of 0.72 and a Jaccard index of 0.67 on the validation set.
+
+### MA-Net
+
+The Multi-scale Attention Net (MA-Net) architecture was implemented to improve heart segmentation. After 100 epochs, this model outperformed U-Net++ with an average Dice coefficient of 0.85 and a Jaccard index of 0.82 on the validation set. Further training for 200 epochs reduced oscillations in the training and validation metrics.
+<p align='center'>
+<img src='https://github.com/dcrovo/DeepLearning-HeartSegmentation/blob/main/imgs/MANET_TRAIN.png?raw=true' />  
+<img src='https://github.com/dcrovo/DeepLearning-HeartSegmentation/blob/main/imgs/MANET_DICE_VAL.png?raw=true' />  
+
+</p>
+
+## Results
+This is a sample CT scan segmented by the MANET model in red and by a certified board radiologist in green.
+<p align='center'>
+<img src='https://github.com/dcrovo/DeepLearning-HeartSegmentation/blob/main/imgs/final.png?raw=true' width=496 height=515/>  
+</p>
+
+
+In summary, the MA-Net model demonstrated superior performance in heart segmentation compared to U-Net++. It achieved a Dice coefficient of 0.883, which compares favourably with state-of-the-art models. However, both models exhibited some overfitting, likely due to the limited size of the training dataset.
+
+## Future Work
+
+To enhance the models further, future work will focus on hyperparameter optimization and increased data augmentation. The well-segmented heart regions will be used as a crucial step for addressing broader medical image analysis challenges.
+