1. Fedorov A. et al (2012). 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network. Magnetic resonance imaging 30, 1323–1341.
2. Diaz-Pinto A. et al (2023) MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images. arXiv:2203.12362 [cs.HC].
3. Diaz-Pinto A. et al (2023). DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images. arXiv:2305.10655 [eess.IV]
4. Ronneberger et al (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv:1505.04597 [cs.CV]
5. Nath V. et al (2021) Diminishing Uncertainty Within the Training Pool: Active Learning for Medical Image Segmentation. IEEE Trans Med Imaging . 40(10):2534-2547
The information used to create this poster can be found on numerous websites, tutorials, and forums related to 3D-Slicer and MONAI Label. There is a highly active community of researchers and enthusiasts in this field, ready to assist in case of any difficulties, to whom our sincere gratitude is extended.
In particular, we highlight the following websites with their respective links:
- https://monai.io/label.html (Quick start guide and documentation)
- https://pypi.org/project/monai/ (info about the installation of MONAI)
- https://github.com/Project-MONAI/MONAI (installation of MONAI through GitHub)
- https://hub.docker.com/r/projectmonai/monai (installation of MONAI through Docker Hub)
- https://www.youtube.com/@ProjectMONAI (The MONAI YouTube channel with numerous tutorials and helpful resources.)
- https://projectweek.na-mic.org/PW39_2023_Montreal/Projects/MONAIBundleIntegrationTutorial/ (how to use MONAI bundle)
- https://monai.io/model-zoo.html (MONAI model zoo website)
- https://www.slicer.org/ (3D-Slicer installation)
- https://discourse.slicer.org/ (The community of 3D-Slicer)
- http://medicaldecathlon.com/ (The Medical Segmentation Decathlon with datasets available for download)