/High-Resolution-Chest-X-ray-Bone-Suppression

Code of High-Resolution Chest X-ray Bone Suppression Using Unpaired CT Structural Priors

Primary LanguagePython

High-Resolution-Chest-X-ray-Bone-Suppression

Code of High-Resolution Chest X-ray Bone Suppression Using Unpaired CT Structural Priors

Requirement:

Python==3.6
Torch==1.1.0
Scipy ==1.1.0   if your Scipy>=1.3.0, you will meet “ImportError: cannot import name 'imresize'”

Get started

There three steps of our work: 1. cycleganxray2gradient, 2. Pytorch-UNet-master_trainbonemask and 3. histogram_match.

Please download our whole project code HERE code: 6orv (including weight, test imges) instead of the code in this github.

Pleas feel free to contact me if you have any problem: han.li[at]miracle.ict.ac.cn / 137412918[at]qq.com

Step1: cycleganxray2gradient

 cd 1cycleganxray2gradient 
 python test.py --dataroot ./datasets --name maps_cyclegan4 --model cycle_gan --epoch 110

Input data location: ‘./datasets/testA’

Output data location: ’ ./results/maps_cyclegan4/test_110/images’

Step2: Pytorch-UNet-master_trainbonemask

cd ..
cd 2Pytorch-UNet-master_trainbonemask
python newpredict.py -c ./checkpoints/CP50.pth

Input data location: Output data location in step1

Output bone data location: ’ ./bone ’

Output lung mask location: ’ ./lung ’

Step3:histogram_match.

cd ..
cd 3histogram_match
python bonemask2result.py

input data location: ’ ./dataset'’

final result location : ‘./result’