--- a/README.md
+++ b/README.md
@@ -1,110 +1,110 @@
-# Hierarchical MRI tumor segmentation with densely connected 3D CNN
-
-By Lele Chen, Yue Wu, [Adora M. DSouza](https://www.rochester.edu/college/gradstudies/profiles/adora-dsouza.html),Anas Z. Abidin, [Axel W. E. Wismuelle](https://www.urmc.rochester.edu/people/27063859-axel-w-e-wismueller), [Chenliang Xu](https://www.cs.rochester.edu/~cxu22/).
-
-University of Rochester.
-
-### Table of Contents
-0. [Introduction](#introduction)
-0. [Citation](#citation)
-0. [Running](#running)
-0. [Model](#model)
-0. [Disclaimer and known issues](#disclaimer-and-known-issues)
-0. [Results](#results)
-
-### Introduction
-
-This repository contains the original models (dense24, dense48, no-dense) described in the paper "Hierarchical MRI tumor segmentation with densely connected 3D CNN" (https://arxiv.org/abs/1802.02427). This code can be applied directly in [BTRAS2017](http://braintumorsegmentation.org/). 
-
-![model](https://github.com/lelechen63/MRI-tumor-segmentation-Brats/blob/master/image/spie.gif)
-
-
-### Citation
-
-If you use these models or the ideas in your research, please cite:
-	
-	@inproceedings{DBLP:conf/miip/ChenWDAWX18,
-	  author    = {Lele Chen and
-		       Yue Wu and
-		       Adora M. DSouza and
-		       Anas Z. Abidin and
-		       Axel Wism{\"{u}}ller and
-		       Chenliang Xu},
-	  title     = {{MRI} tumor segmentation with densely connected 3D {CNN}},
-	  booktitle = {Medical Imaging 2018: Image Processing, Houston, Texas, United States,
-		       10-15 February 2018},
-	  pages     = {105741F},
-	  year      = {2018},
-	  crossref  = {DBLP:conf/miip/2018},
-	  url       = {https://doi.org/10.1117/12.2293394},
-	  doi       = {10.1117/12.2293394},
-	  timestamp = {Tue, 06 Mar 2018 10:50:01 +0100},
-	  biburl    = {https://dblp.org/rec/bib/conf/miip/ChenWDAWX18},
-	  bibsource = {dblp computer science bibliography, https://dblp.org}
-	}
-### Running
-
-
-0. Pre-installation:[Tensorflow](https://www.tensorflow.org/install/),[Ants](https://github.com/ANTsX/ANTs),[nibabel](http://nipy.org/nibabel/),[sklearn](http://scikit-learn.org/stable/),[numpy](http://www.numpy.org/)
-
-0. Download and unzip the training data from [BTRAS2017](http://braintumorsegmentation.org/)
-
-0. Use N4ITK to correct the data: `python n4correction.py /mnt/disk1/dat/lchen63/spie/Brats17TrainingData/HGG`
-0. Train the model:  `python train.py`
-	- `-gpu`: gpu id
-	- `-bs`: batch size 
-	- `-mn`: model name, 'dense24' or 'dense48' or 'no-dense' or 'dense24_nocorrection'
-	- `-nc`:  [n4ITK bias correction](https://www.ncbi.nlm.nih.gov/pubmed/20378467),True or False
-	- `-e`: epoch number 
-	- `-r`: data path
-	- `-sp`: save path/name
-	- ...
-
-For example:
-`python train.py -bs 2 -gpu 0  -mn dense24 -nc True -sp dense48_correction -e 5  -r /mnt/disk1/dat/lchen63/spie/Brats17TrainingData/HGG`
-
-0. Test the model: `python test.py`
-	- `-gpu`: gpu id
-	- `-m`: model path, the saved model name
-	- `-mn`: model name, 'dense24' or 'dense48' or 'no-dense' or 'dense24_nocorrection'
-	- `-nc`:  [n4ITK bias correction](https://www.ncbi.nlm.nih.gov/pubmed/20378467), True or False
-	- `-r`: data path
-	- ...
-
-For example:
-`python test.py -m Dense24_correction-2 -mn dense24 -gpu 0 -nc True  -r /mnt/disk1/dat/lchen63/spie/Brats17TrainingData/HGG`
-
-
-### Model
-
-0. Hierarchical segmentation
-	![model](https://github.com/lelechen63/MRI-tumor-segmentation-Brats/blob/master/image/2.png)
-
-	
-0. 3D densely connected CNN
-
-	![model](https://github.com/lelechen63/MRI-tumor-segmentation-Brats/blob/master/image/1.png)
-
-### Disclaimer and known issues
-
-0. These codes are implmented in Tensorflow
-0. In this paper, we only use the glioblastoma (HGG) dataset.
-0. I didn't config nipype.interfaces.ants.segmentation. So if you need to use `n4correction.py` code, you need to copy it to the bin directory where antsRegistration etc are located. Then run `python n4correction.py`
-0. If you want to train these models using this version of tensorflow without modifications, please notice that:
-	- You need at lest 12 GB GPU memory.
-	- There might be some other untested issues.
-	
-
-### Results
-0. Result visualization :
-	![visualization](https://github.com/lelechen63/MRI-tumor-segmentation-Brats/blob/master/image/h.png)
-	![visualization](https://github.com/lelechen63/MRI-tumor-segmentation-Brats/blob/master/image/v.png)
-
-0. Quantitative results:
-
-	model|whole|peritumoral edema (ED)|FGD-enhan. tumor (ET)
-	:---:|:---:|:---:|:---:
-	Dense24 |0.74| 0.81| 0.80
-	Dense48 | 0.61|0.78|0.79
-	no-dense|0.61|0.77|0.78
-	dense24+n4correction|0.72|0.83|0.81
+# Hierarchical MRI tumor segmentation with densely connected 3D CNN
+
+By Lele Chen, Yue Wu, [Adora M. DSouza](https://www.rochester.edu/college/gradstudies/profiles/adora-dsouza.html),Anas Z. Abidin, [Axel W. E. Wismuelle](https://www.urmc.rochester.edu/people/27063859-axel-w-e-wismueller), [Chenliang Xu](https://www.cs.rochester.edu/~cxu22/).
+
+University of Rochester.
+
+### Table of Contents
+0. [Introduction](#introduction)
+0. [Citation](#citation)
+0. [Running](#running)
+0. [Model](#model)
+0. [Disclaimer and known issues](#disclaimer-and-known-issues)
+0. [Results](#results)
+
+### Introduction
+
+This repository contains the original models (dense24, dense48, no-dense) described in the paper "Hierarchical MRI tumor segmentation with densely connected 3D CNN" (https://arxiv.org/abs/1802.02427). This code can be applied directly in [BTRAS2017](http://braintumorsegmentation.org/). 
+
+![model](https://github.com/lelechen63/MRI-tumor-segmentation-Brats/blob/master/image/spie.gif?raw=true)
+
+
+### Citation
+
+If you use these models or the ideas in your research, please cite:
+	
+	@inproceedings{DBLP:conf/miip/ChenWDAWX18,
+	  author    = {Lele Chen and
+		       Yue Wu and
+		       Adora M. DSouza and
+		       Anas Z. Abidin and
+		       Axel Wism{\"{u}}ller and
+		       Chenliang Xu},
+	  title     = {{MRI} tumor segmentation with densely connected 3D {CNN}},
+	  booktitle = {Medical Imaging 2018: Image Processing, Houston, Texas, United States,
+		       10-15 February 2018},
+	  pages     = {105741F},
+	  year      = {2018},
+	  crossref  = {DBLP:conf/miip/2018},
+	  url       = {https://doi.org/10.1117/12.2293394},
+	  doi       = {10.1117/12.2293394},
+	  timestamp = {Tue, 06 Mar 2018 10:50:01 +0100},
+	  biburl    = {https://dblp.org/rec/bib/conf/miip/ChenWDAWX18},
+	  bibsource = {dblp computer science bibliography, https://dblp.org}
+	}
+### Running
+
+
+0. Pre-installation:[Tensorflow](https://www.tensorflow.org/install/),[Ants](https://github.com/ANTsX/ANTs),[nibabel](http://nipy.org/nibabel/),[sklearn](http://scikit-learn.org/stable/),[numpy](http://www.numpy.org/)
+
+0. Download and unzip the training data from [BTRAS2017](http://braintumorsegmentation.org/)
+
+0. Use N4ITK to correct the data: `python n4correction.py /mnt/disk1/dat/lchen63/spie/Brats17TrainingData/HGG`
+0. Train the model:  `python train.py`
+	- `-gpu`: gpu id
+	- `-bs`: batch size 
+	- `-mn`: model name, 'dense24' or 'dense48' or 'no-dense' or 'dense24_nocorrection'
+	- `-nc`:  [n4ITK bias correction](https://www.ncbi.nlm.nih.gov/pubmed/20378467),True or False
+	- `-e`: epoch number 
+	- `-r`: data path
+	- `-sp`: save path/name
+	- ...
+
+For example:
+`python train.py -bs 2 -gpu 0  -mn dense24 -nc True -sp dense48_correction -e 5  -r /mnt/disk1/dat/lchen63/spie/Brats17TrainingData/HGG`
+
+0. Test the model: `python test.py`
+	- `-gpu`: gpu id
+	- `-m`: model path, the saved model name
+	- `-mn`: model name, 'dense24' or 'dense48' or 'no-dense' or 'dense24_nocorrection'
+	- `-nc`:  [n4ITK bias correction](https://www.ncbi.nlm.nih.gov/pubmed/20378467), True or False
+	- `-r`: data path
+	- ...
+
+For example:
+`python test.py -m Dense24_correction-2 -mn dense24 -gpu 0 -nc True  -r /mnt/disk1/dat/lchen63/spie/Brats17TrainingData/HGG`
+
+
+### Model
+
+0. Hierarchical segmentation
+	![model](https://github.com/lelechen63/MRI-tumor-segmentation-Brats/blob/master/image/2.png?raw=true)
+
+	
+0. 3D densely connected CNN
+
+	![model](https://github.com/lelechen63/MRI-tumor-segmentation-Brats/blob/master/image/1.png?raw=true)
+
+### Disclaimer and known issues
+
+0. These codes are implmented in Tensorflow
+0. In this paper, we only use the glioblastoma (HGG) dataset.
+0. I didn't config nipype.interfaces.ants.segmentation. So if you need to use `n4correction.py` code, you need to copy it to the bin directory where antsRegistration etc are located. Then run `python n4correction.py`
+0. If you want to train these models using this version of tensorflow without modifications, please notice that:
+	- You need at lest 12 GB GPU memory.
+	- There might be some other untested issues.
+	
+
+### Results
+0. Result visualization :
+	![visualization](https://github.com/lelechen63/MRI-tumor-segmentation-Brats/blob/master/image/h.png?raw=true)
+	![visualization](https://github.com/lelechen63/MRI-tumor-segmentation-Brats/blob/master/image/v.png?raw=true)
+
+0. Quantitative results:
+
+	model|whole|peritumoral edema (ED)|FGD-enhan. tumor (ET)
+	:---:|:---:|:---:|:---:
+	Dense24 |0.74| 0.81| 0.80
+	Dense48 | 0.61|0.78|0.79
+	no-dense|0.61|0.77|0.78
+	dense24+n4correction|0.72|0.83|0.81