# 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