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NiftyNet 0.1rc38

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

Latest Version: 0.1.2.post1

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 an initiative of the Wellcome EPSRC Centre for Interventional and Surgical Sciences. 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 (2.5-D: volumetric images processed as a stack of 2D slices; 4-D: co-registered multi-modal 3D volumes)
  • Efficient discriminative training with multiple-GPU support
  • Implemented recent networks (HighRes3DNet, 3D U-net, V-net, DeepMedic)
  • Comprehensive evaluation metrics for medical image segmentation

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}
}

Acknowledgements

This project was supported through an Innovative Engineering for Health award by the Wellcome Trust and EPSRC (WT101957, NS/A000027/1), the National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative), UCL EPSRC CDT Scholarship Award (EP/L016478/1), a UCL Overseas Research Scholarship, a UCL Graduate Research Scholarship, and the Health Innovation Challenge Fund by the Department of Health and Wellcome Trust (HICF-T4-275, WT 97914). The authors would like to acknowledge that the work presented here made use of Emerald, a GPU-accelerated High Performance Computer, made available by the Science & Engineering South Consortium operated in partnership with the STFC Rutherford-Appleton Laboratory.

 
File Type Py Version Uploaded on Size
NiftyNet-0.1rc38-py2-none-any.whl (md5) Python Wheel py2 2017-07-17 115KB