https://github.com/daviddmc/fetal-IQA
Image quality assessment for fetal MRI
https://github.com/daviddmc/fetal-IQA
convolutional-neural-networks deep-learning fetal-mri medical-imaging pytorch quality-control semi-supervised-learning tensorflow
Last synced: about 1 year ago
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Image quality assessment for fetal MRI
- Host: GitHub
- URL: https://github.com/daviddmc/fetal-IQA
- Owner: daviddmc
- License: mit
- Created: 2022-11-25T14:34:01.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-11-30T16:35:19.000Z (over 3 years ago)
- Last Synced: 2024-11-13T21:44:37.073Z (over 1 year ago)
- Topics: convolutional-neural-networks, deep-learning, fetal-mri, medical-imaging, pytorch, quality-control, semi-supervised-learning, tensorflow
- Language: Python
- Homepage:
- Size: 338 KB
- Stars: 6
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Image quality assessment for fetal MRI
This repo is the implementation of an image quality assessment (IQA) method for fetal MRI, which is the accumulation of the following works:
\[1\] Semi-supervised learning for fetal brain MRI quality assessment with ROI consistency ([MICCAI](https://link.springer.com/chapter/10.1007/978-3-030-59725-2_37) | [arXiv](https://arxiv.org/abs/2006.12704))
\[2\] Automated detection and reacquisition of motion-degraded images in fetal HASTE imaging at 3 T ([MRM](https://onlinelibrary.wiley.com/doi/10.1002/mrm.29106))
\[3\] A deep learning approach for image quality assessment of fetal brain MRI ([ISMRM](https://archive.ismrm.org/2019/0839.html))
## Usage
### Train your own models
#### Brain segmentation (optional)
To use ROI consistency, you would need to generate ROI for your dataset.
1. Download the [pre-trained segmentation network](https://bitbucket.org/bchradiology/u-net/src/master/Model/)
2. Modifty `PATH_LABELED_DATA` and `PATH_UNLABELED_DATA` in `brainSeg/Code/FetalUnet.py` to point to your own dataset.
3. Run:
```
cd brainSeg/Code
python FetalUnet.py
```
#### Implement your dataset
Implement your dataset following `src/mean_teacher/haste.py`
#### Training
```
cd src
python experiments/haste_exp.py
```
### Use pre-trained model
#### PyTorch
1. Download [pre-trained models](https://zenodo.org/record/7368570) (`pytorch.ckpt`) to `torch_iqa_tool/pretrained_models`
2. run demo
```
cd torch_iqa_tool
python iqa_demo.py
```
#### Tensorflow
1. Download [pre-trained models](https://zenodo.org/record/7368570) (`model_ismrm.hdf5` and `model_miccai.h5`) to `tf_iqa_tool/pretrained_models`
2. run demo
```
cd tf_iqa_tool
python iqa_demo.py
```
## Cite our work
```
@inproceedings{xu2020semi,
title={Semi-supervised learning for fetal brain MRI quality assessment with ROI consistency},
author={Xu, Junshen and Lala, Sayeri and Gagoski, Borjan and Abaci Turk, Esra and Grant, P Ellen and Golland, Polina and Adalsteinsson, Elfar},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={386--395},
year={2020},
organization={Springer}
}
@article{gagoski2022automated,
title={Automated detection and reacquisition of motion-degraded images in fetal HASTE imaging at 3 T},
author={Gagoski, Borjan and Xu, Junshen and Wighton, Paul and Tisdall, M Dylan and Frost, Robert and Lo, Wei-Ching and Golland, Polina and van Der Kouwe, Andre and Adalsteinsson, Elfar and Grant, P Ellen},
journal={Magnetic Resonance in Medicine},
volume={87},
number={4},
pages={1914--1922},
year={2022},
publisher={Wiley Online Library}
}
@inproceedings{lala2019deep,
title={A deep learning approach for image quality assessment of fetal brain MRI},
author={Lala, Sayeri and Singh, Nalini and Gagoski, Borjan and Turk, Esra and Grant, P Ellen and Golland, Polina and Adalsteinsson, Elfar}
booktitle={Proceedings of the International Society for Magnetic Resonance in Medicine},
year={2019},
}
```