What is NiftyNet?
NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy. NiftyNet’s modular structure is designed for sharing networks and pre-trained models. Using this modular structure you can:
Get started with established pre-trained networks using built-in tools;
Adapt existing networks to your imaging data;
Quickly build new solutions to your own image analysis problems.
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 pretrained 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
*2.5-D: volumetric images processed as a stack of 2D slices; 4-D: co-registered multi-modal 3D volumes
Networks
A number of models from the literature have been (re)implemented in the NiftyNet framework. These are listed below. All networks can be applied in 2D, 2.5D and 3D configurations and are reimplemented from their original presentation with their default parameters.
DeepMedic (Kamnitsas et. al. 2017)
HighRes3dNet (Li et. al. 2017)
ScaleNet (Fidon et. al. 2017)
UNet (Çiçek et. al. 2016)
VNet (Milletari et. al. 2016)
Further details can be found in the GitLab networks section here.
