3D U-Net Convolution Neural Network
Brain Tumor Segmentation (BraTS) Tutorial
Automatic Cranial Implant Design (AutoImpant)
Anatomical Barriers to Cancer Spread (ABCS)
Background
We designed 3DUnetCNN to make it easy to apply and control the training and application of various deep learning models to medical imaging data. The links above give examples/tutorials for how to use this project with data from various MICCAI challenges.
Getting started
Pretrained Models
Got Questions?
See FAQ, raise an issue on GitHub, or email me at [email protected].
Citation
Ellis D.G., Aizenberg M.R. (2021) Trialing U-Net Training Modifications for Segmenting Gliomas Using Open Source Deep Learning Framework. In: Crimi A., Bakas S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science, vol 12659. Springer, Cham. https://doi.org/10.1007/978-3-030-72087-2_4
Additional Citations
Ellis D.G., Aizenberg M.R. (2020) Deep Learning Using Augmentation via Registration: 1st Place Solution to the AutoImplant 2020 Challenge. In: Li J., Egger J. (eds) Towards the Automatization of Cranial Implant Design in Cranioplasty. AutoImplant 2020. Lecture Notes in Computer Science, vol 12439. Springer, Cham. https://doi.org/10.1007/978-3-030-64327-0_6
Ellis, D.G. and M.R. Aizenberg, Structural brain imaging predicts individual-level task activation maps using deep learning. bioRxiv, 2020: https://doi.org/10.1101/2020.10.05.306951