--- a/README.md
+++ b/README.md
@@ -5,7 +5,7 @@
 - Over 3400 labeled MRI images from thousands patients
 - Over 500 MRI images for evaluation  
 
-<img src="imgs/overview.png" width="30%">  
+<img src="https://github.com/JiaPeng1234/MRI-Segmentation-Transformer/blob/master/imgs/overview.png?raw=true" width="30%">  
 
 Used network architectures including 3d u-net, non-local neural network, attention u-net are proposed.  
 
@@ -39,16 +39,16 @@
 
 Its architecture as following:  
 
-<img src="imgs/non-local.PNG" width="70%">
+<img src="https://github.com/JiaPeng1234/MRI-Segmentation-Transformer/blob/master/imgs/non-local.PNG?raw=true" width="70%">
 
 ### 3d U-net as baseline
 Baseline architecture is a 4-stages 3d u-net, as following:  
 
-<img src="imgs/u-net.PNG" width="70%">  
+<img src="https://github.com/JiaPeng1234/MRI-Segmentation-Transformer/blob/master/imgs/u-net.PNG?raw=true" width="70%">  
 
 ## Results
 Achieve an average accurancy of 97% of all classes.  
 
-<img src="imgs/results1.png" width="60%">  
+<img src="https://github.com/JiaPeng1234/MRI-Segmentation-Transformer/blob/master/imgs/results1.png?raw=true" width="60%">  
 
-<img src="imgs/exp001shape.PNG" width="50%"> 
+<img src="https://github.com/JiaPeng1234/MRI-Segmentation-Transformer/blob/master/imgs/exp001shape.PNG?raw=true" width="50%">