diff --git a/README.md b/README.md
index a09966d..159deac 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,5 @@
 # ImageSegmentation With Vnet
-> This is an example of the prostate in transversal T2-weighted MR images Segment from MICCAI Grand Challenge:Prostate MR Image Segmentation 2012
+> This is an example of the prostate in transversal T2-weighted MR images Segment from MICCAI Grand Challenge: Prostate MR Image Segmentation 2012
 ![](promise12_header.png)
 
 ## Prerequisities
@@ -13,18 +13,19 @@ The following dependencies are needed:
 
 ## How to Use
 
-**1、download trained data,download dataset：https://promise12.grand-challenge.org/download/**
+**1. download trained data, download dataset：https://promise12.grand-challenge.org/download/**
 
-**2、the file of PROMISE2012Image.csv,is like this format:
+**2. the file of PROMISE2012Image.csv, is like this format:**
+```  
   D:\Data\PROMISE2012\Augmentation\Image/0_1.bmp
   D:\Data\PROMISE2012\Augmentation\Image/0_10.bmp
   D:\Data\PROMISE2012\Augmentation\Image/0_2.bmp
-  ......
-if you Augmentation trained data path is not D:\Data\PROMISE2012\,you should change the csv file path just like this:using C:\Data\ replace D:\Data\PROMISE2012\.**
+```
+**if your Augmentation trained data path is not D:\Data\PROMISE2012\, you should change the csv file path just like this: using C:\Data\ replace D:\Data\PROMISE2012\.**
 
-**3、when data is prepared,just run the vnet_train_predict.py**
+**3. when data is prepared, just run the vnet_train_predict.py**
 
-**4、training the model on the GTX1080,it take 20 hours,and i also attach the trained model in the project,you also just use the vnet_train_predict.py file to predict,and get the segmentation result.**
+**4. training the model on the GTX1080, it take 20 hours, and I also attach the trained model in the project, you also just use the vnet_train_predict.py file to predict,and get the segmentation result.**
 
 **5、download trained model:https://pan.baidu.com/s/19E9q6HIUeRB8jpuNhvE2Zg, passworld:obwu**