By Lele Chen, Yue Wu, Adora M. DSouza,Anas Z. Abidin, Axel W. E. Wismuelle, Chenliang Xu.
University of Rochester.
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.
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}
}
Pre-installation:Tensorflow,Ants,nibabel,sklearn,numpy
Download and unzip the training data from BTRAS2017
Use N4ITK to correct the data: python n4correction.py /mnt/disk1/dat/lchen63/spie/Brats17TrainingData/HGG
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,True or False-e
: epoch number -r
: data path-sp
: save path/nameFor 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
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, True or False-r
: data pathFor example:
python test.py -m Dense24_correction-2 -mn dense24 -gpu 0 -nc True -r /mnt/disk1/dat/lchen63/spie/Brats17TrainingData/HGG
Hierarchical segmentation
3D densely connected CNN
n4correction.py
code, you need to copy it to the bin directory where antsRegistration etc are located. Then run python n4correction.py
Result visualization :
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 |