NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy.
NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy.
The code is available via GitLab, or you can quickly get started with the PyPI module available here.
Features
NiftyNet currently supports medical image segmentation and generative adversarial networks. NiftyNet is not intended for clinical use. Other features of NiftyNet include:
- Easy-to-customise interfaces of network components
- Sharing networks and pre-trained models
- Support for 2-D, 2.5-D, 3-D, 4-D inputs
- Efficient discriminative training with multiple-GPU support
- Implementation of recent networks (HighRes3DNet, 3D U-net, V-net, DeepMedic)
- Comprehensive evaluation metrics for medical image segmentation
NiftyNet is released under the Apache License, Version 2.0. Please see the LICENSE file in the NiftyNet source code repository for details.
If you use NiftyNet in your work, please cite Gibson, Li et al. 2017. The NiftyNet platform originated in software developed for Li et. al. 2017.
