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H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes, TMI 2018.
by Xiaomeng Li, Hao Chen, Xiaojuan Qi, Qi Dou, Chi-Wing Fu, Pheng-Ann Heng.
Introduction
This repository is for our TMI 2018 paper 'H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes'.
Environment:
This code is only tested under python2. Check code environment "requirements.txt"
Usage
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Data preprocessing: Download dataset from: Liver Tumor Segmentation Challenge.
Then put 131 training data with segmentation masks under "data/TrainingData/" and 70 test data under "data/TestData/".
Run:python preprocessing.py
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Test our model: Download liver mask from LiverMask and put them in the folder: 'livermask'.
Download model from Model and put them in the folder: 'model'. run:python test.py
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Train 2D DenseUnet: First, you need to download the pretrained model from ImageNet Pretrained, extract it and put it in the folder 'model'. Then run:
sh bash_train.sh
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Train H-DenseUnet: Load your trained model and run
CUDA_VISIBLE_DEVICES='0' python train_hybrid.py -arch 3dpart
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Train H-DenseUnet in end-to-end way:
CUDA_VISIBLE_DEVICES='0' python train_hybrid.py -arch end2end
Citation
If H-DenseUNet is useful for your research, please consider citing:
@article{li2018h,
title={H-denseunet: Hybrid densely connected unet for liver and tumor segmentation from ct volumes},
author={Li, Xiaomeng and Chen, Hao and Qi, Xiaojuan and Dou, Qi and Fu, Chi-Wing and Heng, Pheng-Ann},
journal={IEEE transactions on medical imaging},
volume={37},
number={12},
pages={2663--2674},
year={2018},
publisher={IEEE}
}
Questions
Please contact 'xmli@cse.cuhk.edu.hk'