{"id":13710790,"url":"https://github.com/daviddmc/fetal-IQA","last_synced_at":"2025-05-06T19:32:35.767Z","repository":{"id":111685994,"uuid":"570580676","full_name":"daviddmc/fetal-IQA","owner":"daviddmc","description":"Image quality assessment for fetal MRI","archived":false,"fork":false,"pushed_at":"2022-11-30T16:35:19.000Z","size":346,"stargazers_count":6,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-11-13T21:44:37.073Z","etag":null,"topics":["convolutional-neural-networks","deep-learning","fetal-mri","medical-imaging","pytorch","quality-control","semi-supervised-learning","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/daviddmc.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-11-25T14:34:01.000Z","updated_at":"2024-10-12T16:21:40.000Z","dependencies_parsed_at":"2023-03-13T13:34:55.824Z","dependency_job_id":null,"html_url":"https://github.com/daviddmc/fetal-IQA","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/daviddmc%2Ffetal-IQA","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/daviddmc%2Ffetal-IQA/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/daviddmc%2Ffetal-IQA/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/daviddmc%2Ffetal-IQA/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/daviddmc","download_url":"https://codeload.github.com/daviddmc/fetal-IQA/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252753740,"owners_count":21799003,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["convolutional-neural-networks","deep-learning","fetal-mri","medical-imaging","pytorch","quality-control","semi-supervised-learning","tensorflow"],"created_at":"2024-08-02T23:01:00.894Z","updated_at":"2025-05-06T19:32:30.749Z","avatar_url":"https://github.com/daviddmc.png","language":"Python","funding_links":[],"categories":["Quality Assessment"],"sub_categories":[],"readme":"# Image quality assessment for fetal MRI\nThis repo is the implementation of an image quality assessment (IQA) method for fetal MRI, which is the accumulation of the following works:\n\n\\[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))\n\n\\[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))\n\n\\[3\\] A deep learning approach for image quality assessment of fetal brain MRI ([ISMRM](https://archive.ismrm.org/2019/0839.html))\n\n## Usage\n\n### Train your own models\n\n#### Brain segmentation (optional)\n\nTo use ROI consistency, you would need to generate ROI for your dataset.\n\n1. Download the [pre-trained segmentation network](https://bitbucket.org/bchradiology/u-net/src/master/Model/)\n2. Modifty `PATH_LABELED_DATA` and `PATH_UNLABELED_DATA` in `brainSeg/Code/FetalUnet.py` to point to your own dataset.\n3. Run:\n    ```\n    cd brainSeg/Code\n    python FetalUnet.py\n    ```\n\n#### Implement your dataset\n\nImplement your dataset following `src/mean_teacher/haste.py`\n\n#### Training\n\n```\ncd src\npython experiments/haste_exp.py\n```\n\n### Use pre-trained model\n\n#### PyTorch\n\n1. Download [pre-trained models](https://zenodo.org/record/7368570) (`pytorch.ckpt`) to `torch_iqa_tool/pretrained_models`\n\n2. run demo\n    ```\n    cd torch_iqa_tool\n    python iqa_demo.py\n    ```\n\n#### Tensorflow\n\n1. Download [pre-trained models](https://zenodo.org/record/7368570) (`model_ismrm.hdf5` and `model_miccai.h5`) to `tf_iqa_tool/pretrained_models`\n\n2. run demo\n    ```\n    cd tf_iqa_tool\n    python iqa_demo.py\n    ```\n\n## Cite our work\n```\n@inproceedings{xu2020semi,\n  title={Semi-supervised learning for fetal brain MRI quality assessment with ROI consistency},\n  author={Xu, Junshen and Lala, Sayeri and Gagoski, Borjan and Abaci Turk, Esra and Grant, P Ellen and Golland, Polina and Adalsteinsson, Elfar},\n  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},\n  pages={386--395},\n  year={2020},\n  organization={Springer}\n}\n\n@article{gagoski2022automated,\n  title={Automated detection and reacquisition of motion-degraded images in fetal HASTE imaging at 3 T},\n  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},\n  journal={Magnetic Resonance in Medicine},\n  volume={87},\n  number={4},\n  pages={1914--1922},\n  year={2022},\n  publisher={Wiley Online Library}\n}\n\n@inproceedings{lala2019deep,\n  title={A deep learning approach for image quality assessment of fetal brain MRI},\n  author={Lala, Sayeri and Singh, Nalini and Gagoski, Borjan and Turk, Esra and Grant, P Ellen and Golland, Polina and Adalsteinsson, Elfar}\n  booktitle={Proceedings of the International Society for Magnetic Resonance in Medicine},\n  year={2019},\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdaviddmc%2Ffetal-IQA","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdaviddmc%2Ffetal-IQA","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdaviddmc%2Ffetal-IQA/lists"}