MedicalImageScriptDemo
Description
Some simple medical image processing python script. Wish these code can help you.
Category
1. raw2mha
Raw volume data to mha format.
2. mha2jpg
Mha volume data to jpg slice.
3. resample
Resample volume to specified physical size.
4. lungSeg
Use traditional image processing method to get lung mask from thoracic volume.
5. hairRemoval
Use traditional image processing to remove hair from skin image.
6. readDicom
Read dicom and visualization.
7. volumeRenderingQT
Volume rendering by using VTK, UI by using PyQT5
8. NiftyRegPythonWrapper
NiftyReg is a nice medical image registration tools, see: http://cmictig.cs.ucl.ac.uk/wiki/index.php/NiftyReg
Sometimes, we want to do batch operation, we can use python to control the process. I have already built a NiftyReg in windows platform, the bin files are in the win_bin.
9. Nonfinite-modality data augmentation for brain image registration
This is our published paper for brain image registration. We synthesize some "brain" image to augment the exist deep learning-based brain image registration method. Please see: https://doi.org/10.1016/j.compbiomed.2022.105780 and the relative repo is https://github.com/MangoWAY/SMIBID_BrainRegistration
10. Hierarchical anatomical structure-aware based thoracic CT images registration
This is our published paper for thoracic CT images registration. We used the structure-aware based strategy and FFD-based framework to register the thoracic CT images. Please see: https://doi.org/10.1016/j.compbiomed.2022.105876 and the relative repo is https://github.com/heluxixue/Structure_Aware_Registration
Useful resources
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Use Deep learning method to segment lung, it is a good work can be directly used to your data. see: https://github.com/JoHof/lungmask
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If you want to get some test data, you can see: https://www.dicomlibrary.com/ download dicom data, and convert to any format.
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A very nice medical image processing,visualization software,
3DSlicer: https://www.slicer.org/ -
A nice biomedical imaging competition site, you can find a lot of medical dataset: https://grand-challenge.org/
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If you work on thoracic volume (lung), you can use Dir-lab data: https://med.emory.edu/departments/radiation-oncology/research-laboratories/deformable-image-registration/index.html
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If you study in medical image registration, for modern implement (auto-diff and GPU speed-up), use Air-lab: https://github.com/airlab-unibas/airlab
Cite
If you think my work can help, you can cite my work.
@software{Aoyu_Medical_Image_Script,
author = {Wang, Aoyu},
month = {5},
title = {{Medical Image Script Demo}},
url = {https://github.com/MangoWAY/medicalImageScriptDemo},
version = {0.1},
year = {2022}
}
For paper
@article{HE2022105780,
title = {Nonfinite-modality data augmentation for brain image registration},
journal = {Computers in Biology and Medicine},
volume = {147},
pages = {105780},
year = {2022},
issn = {0010-4825},
doi = {https://doi.org/10.1016/j.compbiomed.2022.105780},
url = {https://www.sciencedirect.com/science/article/pii/S0010482522005479},
author = {Yuanbo He and Aoyu Wang and Shuai Li and Yikang Yang and Aimin Hao},
keywords = {Nonfinite-modality, Data augmentation, Improved 3D VAE, Brain image registration}
}
@article{HE2022105876,
title = {Hierarchical anatomical structure-aware based thoracic CT images registration},
journal = {Computers in Biology and Medicine},
pages = {105876},
year = {2022},
issn = {0010-4825},
doi = {https://doi.org/10.1016/j.compbiomed.2022.105876},
url = {https://www.sciencedirect.com/science/article/pii/S001048252200628X},
author = {Yuanbo He and Aoyu Wang and Shuai Li and Aimin Hao},
keywords = {Thoracic CT registration, Anatomical structure-aware strategy, Deformation ability-aware dissimilarity metric, Motion pattern-aware regularization, A novel hierarchical strategy}
}