S-cuda: Self-Cleansing Unsupervised Domain Adaptation for Medical Image Segmentation

This repository provides code for the paper, S-CUDA: Self-Cleansing Unsupervised Domain Adaptation for Medical Image Segmentation. Please refer to our paper for better understanding the method. Notably, the N1 or N2 in the framework can be replaced to other segmentation networks based on your tasks.

Pipeline

Getting started

Environments

• python 3.5
• tensorflow 1.4.0
• keras 2.2.0
• pytorch 0.4.0
• CUDA 9.2

Packages

• tqdm
• skimage
• opencv
• scipy
• matplotlib

Datasets

Download from Refuge, prepare dataset in data directory as follows.

S-cuda
│   Network-1
|   Network-2
│   scripts
└───dataset
│   │   source
│   │   │   images
│   │   │   labels
│   │
│   └───target
│   │   │   images
│   │   │   labels
│   │   │   pseudo_label 
│   │ 
│   └───test
│   │   │   images
│   │   │   labels
│   │
│   └───source.txt
│   │   target.txt
│   │   test.txt
│        
└───README.md

Initial weights and pre-trained model

Initial weights and pre-trained model download link:

• Initial weights, code:4y69
• Pre-trained model, code:9koj

unzip Initial_weights.zip 
unzip Pre-trained model.zip 

Running

0.Clone this repo:

git clone https://github.com/zzdxjtu/S-cuda.git
cd S-cuda

1.Train:
All training script is stored in scripts directory.

sh scripts/run1.sh  

##The two peer networks select the noise data and clean data simultaneously, and then the selected clean data are fed into the peer network for finetune, and you can change the remember rate and noise rate according to the noise ratio of the source training set.

sh scripts/run2.sh  

##The two networks select the noise data and clean data simultaneously, and then input the clean data into each other's network for finetune.

sh scripts/run3.sh  

##Correct the common part of the noise data selected by the two networks, and then the two networks select the noise data and clean data simultaneously, at last input the clean data into each other's network for finetune.

sh scripts/run4.sh  

##Correct the common part of the noise data selected by the two networks, and then the two networks select the noise data and clean data simultaneously, at last input the clean data into each other's network for finetune.
Before training, please check whether all the model weight and dataset path is correct.
2.Test:

cd S-cuda
python Network-1/evaluation.py

3.Performance

Pretrained

Level_0.5-0.7/noise_labels_0.5	select_0.1	select_0.2	select_0.3	select_0.4
Disc_dice	0.95	0.95	0.948	0.95
Cup_dice	0.882	0.873	0.871	0.871

Scratch

Level_0.5-0.7/noise_labels_0.5	select_0.1	select_0.2	select_0.3	select_0.4
Disc_dice	0.947	0.943	0.943	0.941
Cup_dice	0.889	0.886	0.886	0.872

4.Selection ability
We also demonstrate the selection ability of these two networks training from scratch via calculating the dice coefficient.

Clean data

N1/Level_0.5-0.7/noise_labels_0.1	select_0.1	select_0.3	select_0.5	select_0.7
Disc_dice	1.0	0.995	0.997	0.994
Cup_dice	1.0	1.0	1.0	0.995

N2/Level_0.5-0.7/noise_labels_0.1	select_0.1	select_0.3	select_0.5	select_0.7
Disc_dice	1.0	1.0	0.995	0.983
Cup_dice	1.0	1.0	0.982	0.967

Noisy data

N1/Level_0.5-0.7/noise_labels_0.1	select_0.1_1	select_0.1_2	select_0.1_3	select_0.1_4
Disc_dice	0.861	0.896	0.786	0.815
Cup_dice	0.784	0.777	0.755	0.813

N2/Level_0.5-0.7/noise_labels_0.1	select_0.1_1	select_0.1_2	select_0.1_3	select_0.1_4
Disc_dice	0.857	0.864	0.833	0.897
Cup_dice	0.766	0.801	0.827	0.925

Intersection of N1 and N2/Level_0.5-0.7/noise_labels_0.1	select_0.1_1	select_0.1_2	select_0.1_3	select_0.1_4
Disc_dice	0.761	0.831	0.7	0.743
Cup_dice	0.615	0.596	0.715	0.866

In addition, the noise select ratio and clean select ratio can be modified according to your own tasks and the training dataset. For example, with the noise ratio of the dataset, which can be estimated by sampling analysis, you can set the select ratio accordingly. You can also set both of the initial noise select ratio and clean select ratio to 0.1, respectively, and then increase the select ratio of clean data gradually to make sure the two peer netowrks work well, after that maintain the select ratio of clean data and gradually increase the select ratio of noisy data.

Supplementary notes

boudary.ipynb  
##Calculate the weight map of the optic cup, optic disc, and background  
calculate_dice.py  
##Calculate dice coefficient  
get_contour.ipynb  
##Obtain the edge contour of the target object  
hausdorff_dis.py  
##Calculate hausdorff distance  
noise_label.ipynb  
##Generate labels with different levels of noise and different proportions, including corrosion, expansion, deformation operations
crop.py  
##crop 512×512 ROI from source image

Acknowledge

Some of our codes (i.e., two peer networks) are referring to liyunsheng13/BDL and EmmaW8/pOSAL. Thanks for their helpful works.