--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-![image](https://github.com/mdholbrook/MRI_Segmentation_Radiomics/blob/master/.github/CIVMBanner.png)
+![image](https://github.com/mdholbrook/MRI_Segmentation_Radiomics/blob/master/.github/CIVMBanner.png?raw=true)
 # MRI Segmentation and Radiomics
 This repository contains example code from the paper in preparation on preclinical cancer imaging titled "MRI-based 
 Deep Learning Segmentation and Radiomics of Sarcoma Tumors in Mice."
@@ -20,12 +20,12 @@
 ## Segmentation
 Segmentation was performed via a U-net CNN. The network functions on patches taken from image volumes. The general 
 network structure is shown below.
-![image](https://github.com/mdholbrook/MRI_Segmentation_Radiomics/blob/master/.github/cnn_structure.png)
+![image](https://github.com/mdholbrook/MRI_Segmentation_Radiomics/blob/master/.github/cnn_structure.png?raw=true)
 
 Training and perfomance anlysis is done using the [Segmentation.py](Segmentation/Segmentation.py) script. The results
  for a network trained on multi-contrast MR images with cross entropy loss is shown below.
  
- ![image](https://github.com/mdholbrook/MRI_Segmentation_Radiomics/blob/master/.github/segmentations.png)
+ ![image](https://github.com/mdholbrook/MRI_Segmentation_Radiomics/blob/master/.github/segmentations.png?raw=true)
 
 #### Requirements
 T2-weighted images are bias corrected using N4BiasFieldCorrection in [ANTs](http://stnava.github.io/ANTs/).
@@ -48,7 +48,7 @@
  
 Using this code, we achieved an AUC of 0.81 for predicting recurrence within these mice.
  
-![image](https://github.com/mdholbrook/MRI_Segmentation_Radiomics/blob/master/.github/classifier_results.png)
+![image](https://github.com/mdholbrook/MRI_Segmentation_Radiomics/blob/master/.github/classifier_results.png?raw=true)
  
 #### Requirements
 Due to the high dimensionality of the data (321 features per tumor) feature selection is required