WPSS

The project repository for Weakly supervised perivascular spaces segmentation with salient guidance of Frangi filter (WPSS). WPSS is a weakly supervised convolutional neural network model which focuses on segmenting perivascular spaces (PVS) in the white matter regions.

Acknowledgement

If you use WPSS code, please cite the following paper:

Weakly upervised Perivascular Spaces Segmentation with salient guidance of Frangi filter
Haoyu Lan, Kirsten M. Lynch, Rachel Custer, Nien‐Chu Shih, Patrick Sherlock, Arthur W. Toga, Farshid Sepehrband, Jeiran Choupan

For data inquiries, please contact me and Dr. Choupan by emails in the publication.

Data

Both training and inference datasets should be organized as the following structure:

dataset
│
└───subject 1
|   |    epc.nii.gz
|   |    mask.nii.gz(optional)
|   |    target.nii.gz(not required for the inference dataset)
└───subject 2
|   |    epc.nii.gz
|   |    mask.nii.gz
|   |    target.nii.gz
└───subject 3
|   |    epc.nii.gz
|   |    mask.nii.gz
|   |    target.nii.gz
│   ...

Human Connectome Project (HCP) dataset was used for the WPSS training and evaluation.

The Enhanced PVS Contrast (EPC) modality and training target generations are upon request. Please refer to the original publication for additional information of EPC modality.

Requirements

python 3 is required and python 3.6.4 was used in the study.

Requirements can be found at requirements.txt.

Please use pip install requirements.txt to install the requirements

How to run the code

Use the following command for the model training and inference. Pretrained weights with quality controlled labels for the model inference using either the EPC modality or the T1w modality are upon request.

Training:

Training script is at ./src/training

Use the following command to run the training script:

python training.py --trainig_size= --gpu= --num_iter= --patch_size= --dataset= --modality= --logdir= --output= --val_portion=

configurations	meaning	default
--training_size	the number of training data to use	None
--gpu	gpu ID for training	None
--val_portion	percentage of the training dataset used for the validation	0.25
--num_iter	the number of iteration	3000
--patch_size	input image size (same for three dimensions)	16
--dataset	path to the training dataset	None
--modality	names of the input image and the target image	epc_frangi
--logdir	path to the saved tensorboard log	None
--output	path to saved images during training	None
--lr	learning rate	0.001
--mask	if using the mask for the evaluation on validation data (mask should be save as "mask.nii.gz")	False

Inference:

Inference script is at ./src/predict. The corresponding PVS segmentation results will be saved under the same directory as the inference dataset.

Use the following command to run the inference script:

python predict.py --gpu= --weights= --dataset= --modality=

configurations	meaning	default
--gpu	gpu ID for inference	0
--weights	path of trained model weights to load	None
--patch_size	patch size for three dimensions	16
--dataset	path to the inference dataset	None
--modality	name of the modality used as the input (could be either "epc" or "t1w")	epc

License

If you use this code for your research, please be familiar with the LICENSE and cite our paper.

Citation

@article{lan2023weakly,
  title={Weakly supervised perivascular spaces segmentation with salient guidance of Frangi filter},
  author={Lan, Haoyu and Lynch, Kirsten M and Custer, Rachel and Shih, Nien-Chu and Sherlock, Patrick and Toga, Arthur W and Sepehrband, Farshid and Choupan, Jeiran},
  journal={Magnetic Resonance in Medicine},
  volume={89},
  number={6},
  pages={2419--2431},
  year={2023},
  publisher={Wiley Online Library}
}

Haoyulance / WPSS