t110368027/SSMIS

Semi-Supervised-Medical-Image-Segmentation-Benchmark

Semi-Supervised-Medical-Image-Segmentation(SSMIS) Benchmark

📝 Description

This Project is focus on Semi Supervised Medical Image Segmentation

📊 Datasets

• DCA1
  • Cervantes-Sanchez, Fernando, et al. "Automatic segmentation of coronary arteries in X-ray angiograms using multiscale analysis and artificial neural networks." Applied Sciences 9.24 (2019): 5507.
• STARE
  • Hoover, A. D., Valentina Kouznetsova, and Michael Goldbaum. "Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response." IEEE Transactions on Medical imaging 19.3 (2000): 203-210.

💻 Environments

• OS: Windows 10 / Linux Ubuntu 18
• GPU: NVIDIA TAITAN Xp GPU / NVIDIA Tesla V100 GPU
• Pytorch: 2.0.1 cuda 11.7
  • https://pytorch.org/get-started/locally/ / https://github.com/pytorch/pytorch
• Pytorch Lightning: 2.0.6
  • https://www.pytorchlightning.ai/index.html / https://github.com/Lightning-AI/lightning
• Albumentations: 1.3.1
  • https://github.com/albumentations-team/albumentations/
• MONAI: 1.2.0
  • https://github.com/Project-MONAI/MONAI

📜 Acknowledgements

• SSL4MIS

🔮 Questions and Suggestions

If you have any questions or suggestions about this project, please contact me through email: t110368027@ntut.org.tw