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A convolutional neural networks platform for research in medical image analysis and computer-assisted intervention.

Project Description

NiftyNet

NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNN) platform for research in medical image analysis and computer-assisted intervention. NiftyNet is a consortium of multiple research groups (WEISS – Wellcome EPSRC Centre for Interventional and Surgical Sciences, CMIC – Centre for Medical Image Computing, HIG – High-dimensional Imaging Group), where WEISS acts as a consortium lead. NiftyNet is not intended for clinical use.

Features

  • Easy-to-customise interfaces of network components
  • Designed for sharing networks and pretrained models
  • Designed to support 2-D, 2.5-D, 3-D, 4-D inputs [1]
  • Efficient discriminative training with multiple-GPU support
  • Implemented recent networks (HighRes3DNet, 3D U-net, V-net, DeepMedic)
  • Comprehensive evaluation metrics for medical image segmentation
[1]2.5-D: volumetric images processed as a stack of 2D slices; 4-D: co-registered multi-modal 3D volumes

Getting started and contributing

Please follow the instructions on the NiftyNet source code repository.

Citing NiftyNet

If you use NiftyNet, please cite the following paper:

@InProceedings{niftynet17,
  author = {Li, Wenqi and Wang, Guotai and Fidon, Lucas and Ourselin, Sebastien and Cardoso, M. Jorge and Vercauteren, Tom},
  title = {On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task},
  booktitle = {International Conference on Information Processing in Medical Imaging (IPMI)},
  year = {2017}
}
Release History

Release History

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