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Huabing Liu*, Jiawei Huang, Dengqiang Jia, Qian Wang, Jun Xu, and Dinggang Shen

@article{liu2024transferring,title={Transferring Adult-like Phase Images for Robust Multi-view Isointense Infant Brain Segmentation},author={Liu, Huabing and Huang, Jiawei and Jia, Dengqiang and Wang, Qian and Xu, Jun and Shen, Dinggang},journal={IEEE Transactions on Medical Imaging},year={2024},publisher={IEEE}}

This repo includes the source codes and pretrained models for our latest work on isointense infant brain segmentation. The two major components are 1) disentangled cycle-consistent adversarial network (dcan) for style transfer between isointense and adult-like phase images; 2) the segmentation networkcoseg that implements multi-view learning to incorporate adult-like phase images in isointense infant brain segmentation. If you find this repo useful, please give it a star ⭐ and consider citing our paper in your research. Thank you.

• Python 3 (Recommend to useAnaconda)

• NVIDIA GPU +Pytorch

• Python packages:

pip install -r requirements.txt

Build up the workspace, so that everything can be correctly stored:

sh install.sh

For your own dataset, format each data as:

|--<name_of_the_data>|-- t1.nii.gz|-- t2.nii.gz|-- seg.nii.gz

for T1-weighted images, T2-weighted images, and segmentation (if exists), respectively.

Then put formatted data into correct folders:

• for isointense phase images, put them into <pwd>/dcan/data/raw/6m
• for adult-like phase images, put them into <pwd>/dcan/data/raw/12m

Suppose <pwd> is the directory of this repo

For test-only purpose of this repo, we have shared all the pretrained models:

Put downloaded *.pth into Results folders

Run proc.ipynb

Modify the proc.ipynb:

• for processing isointense phase images:

data_path ='data/raw/6m'out_path ='data/processed/6m'

• for processing adult-like phase images:

data_path ='data/raw/12m'out_path ='data/processed/12m'

Python3 train.py

Run syn.ipynb

Modify the syn.ipynb:

• for transferring isointense phase images to adult-like contrast

config.dataset.src_dir ='data/processed/6m'config.dataset.dst_dir ='data/processed/12m'

• for transferring adult-like phase images to isointense contrast:

config.dataset.src_dir ='data/processed/12m'config.dataset.dst_dir ='data/processed/6m'

Run proc.ipynb

Modify the proc.ipynb

• for processing source isointense phase images:

data_path ='../dcan/data/processed/6m'out_path ='data/processed/6m'

• for processing synthetic isointense phase images:

data_path ='../dcan/data/syn/6m'out_path ='data/syn/6m'

• for processing source adult-like phase images:

data_path ='../dcan/data/processed/12m'out_path ='data/processed/12m'

• for processing synthetic adult-like phase images:

data_path ='../dcan/data/syn/12m'out_path ='data/syn/12m'

sh train.sh

python3 test.py

If you find any bugs upon running this repo, please raise an issue in the github page or send me an email (hbliu98@gmail.com).