All Projects → NifTK → Niftynet

NifTK / Niftynet

Licence: apache-2.0
[unmaintained] An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy

Programming Languages

python
139335 projects - #7 most used programming language
python3
1442 projects
python2
120 projects

Projects that are alternatives of or similar to Niftynet

Miscnn
A framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning
Stars: ✭ 194 (-84.8%)
Mutual labels:  convolutional-neural-networks, segmentation, pip, medical-imaging
Oneflow
OneFlow is a performance-centered and open-source deep learning framework.
Stars: ✭ 2,868 (+124.76%)
Mutual labels:  deep-neural-networks, ml, distributed
Tfmesos
Tensorflow in Docker on Mesos #tfmesos #tensorflow #mesos
Stars: ✭ 194 (-84.8%)
Mutual labels:  deep-neural-networks, ml, distributed
Pointcnn
PointCNN: Convolution On X-Transformed Points (NeurIPS 2018)
Stars: ✭ 1,120 (-12.23%)
Mutual labels:  deep-neural-networks, convolutional-neural-networks, segmentation
Hyperdensenet
This repository contains the code of HyperDenseNet, a hyper-densely connected CNN to segment medical images in multi-modal image scenarios.
Stars: ✭ 124 (-90.28%)
Mutual labels:  deep-neural-networks, convolutional-neural-networks, segmentation
Kiu Net Pytorch
Official Pytorch Code of KiU-Net for Image Segmentation - MICCAI 2020 (Oral)
Stars: ✭ 134 (-89.5%)
Mutual labels:  deep-neural-networks, segmentation, medical-imaging
Quicknat pytorch
PyTorch Implementation of QuickNAT and Bayesian QuickNAT, a fast brain MRI segmentation framework with segmentation Quality control using structure-wise uncertainty
Stars: ✭ 74 (-94.2%)
Mutual labels:  convolutional-neural-networks, segmentation, medical-imaging
Awesome Gan For Medical Imaging
Awesome GAN for Medical Imaging
Stars: ✭ 1,814 (+42.16%)
Mutual labels:  gan, segmentation, medical-imaging
Zhihu
This repo contains the source code in my personal column (https://zhuanlan.zhihu.com/zhaoyeyu), implemented using Python 3.6. Including Natural Language Processing and Computer Vision projects, such as text generation, machine translation, deep convolution GAN and other actual combat code.
Stars: ✭ 3,307 (+159.17%)
Mutual labels:  convolutional-neural-networks, gan, autoencoder
All About The Gan
All About the GANs(Generative Adversarial Networks) - Summarized lists for GAN
Stars: ✭ 630 (-50.63%)
Mutual labels:  gan, segmentation, medical-imaging
Torchio
Medical image preprocessing and augmentation toolkit for deep learning
Stars: ✭ 708 (-44.51%)
Mutual labels:  convolutional-neural-networks, segmentation, medical-imaging
Gpnd
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
Stars: ✭ 112 (-91.22%)
Mutual labels:  deep-neural-networks, gan, autoencoder
Awesome Tensorlayer
A curated list of dedicated resources and applications
Stars: ✭ 248 (-80.56%)
Mutual labels:  convolutional-neural-networks, segmentation, autoencoder
Livianet
This repository contains the code of LiviaNET, a 3D fully convolutional neural network that was employed in our work: "3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study"
Stars: ✭ 143 (-88.79%)
Mutual labels:  deep-neural-networks, convolutional-neural-networks, medical-imaging
Data Science Bowl 2018
End-to-end one-class instance segmentation based on U-Net architecture for Data Science Bowl 2018 in Kaggle
Stars: ✭ 56 (-95.61%)
Mutual labels:  convolutional-neural-networks, segmentation, medical-imaging
Tensorflow
An Open Source Machine Learning Framework for Everyone
Stars: ✭ 161,335 (+12543.81%)
Mutual labels:  deep-neural-networks, ml, distributed
Cascaded Fcn
Source code for the MICCAI 2016 Paper "Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional NeuralNetworks and 3D Conditional Random Fields"
Stars: ✭ 296 (-76.8%)
Mutual labels:  deep-neural-networks, segmentation, medical-imaging
Medicaldetectiontoolkit
The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.
Stars: ✭ 917 (-28.13%)
Mutual labels:  deep-neural-networks, segmentation, medical-imaging
Dltk
Deep Learning Toolkit for Medical Image Analysis
Stars: ✭ 1,249 (-2.12%)
Mutual labels:  deep-neural-networks, ml, medical-imaging
Deep Atrous Cnn Sentiment
Deep-Atrous-CNN-Text-Network: End-to-end word level model for sentiment analysis and other text classifications
Stars: ✭ 64 (-94.98%)
Mutual labels:  deep-neural-networks, convolutional-neural-networks

Status update - 2020-04-21

⚠️ NiftyNet is not actively maintained anymore. We have learned a lot in our journey and decided to redirect most of the development efforts towards MONAI.

NiftyNet

pipeline status coverage report License PyPI version

NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNN) 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

NiftyNet is a consortium of research organisations (BMEIS -- School of Biomedical Engineering and Imaging Sciences, King's College London; WEISS -- Wellcome EPSRC Centre for Interventional and Surgical Sciences, UCL; CMIC -- Centre for Medical Image Computing, UCL; HIG -- High-dimensional Imaging Group, UCL), where BMEIS acts as the consortium lead.

Features

  • 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 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 not intended for clinical use.

NiftyNet release notes are available here.

*2.5-D: volumetric images processed as a stack of 2D slices; 4-D: co-registered multi-modal 3D volumes

Installation

  1. Please install the appropriate TensorFlow package*:
  2. pip install niftynet

All other NiftyNet dependencies are installed automatically as part of the pip installation process.

To install from the source repository, please checkout the instructions.

Documentation

The API reference and how-to guides are available on Read the Docs.

Useful links

Citing NiftyNet

If you use NiftyNet in your work, please cite Gibson and Li, et al. 2018:

BibTeX entry:

@article{Gibson2018,
  title = "NiftyNet: a deep-learning platform for medical imaging",
  journal = "Computer Methods and Programs in Biomedicine",
  year = "2018",
  issn = "0169-2607",
  doi = "https://doi.org/10.1016/j.cmpb.2018.01.025",
  url = "https://www.sciencedirect.com/science/article/pii/S0169260717311823",
  author = "Eli Gibson and Wenqi Li and Carole Sudre and Lucas Fidon and
            Dzhoshkun I. Shakir and Guotai Wang and Zach Eaton-Rosen and
            Robert Gray and Tom Doel and Yipeng Hu and Tom Whyntie and
            Parashkev Nachev and Marc Modat and Dean C. Barratt and
            Sébastien Ourselin and M. Jorge Cardoso and Tom Vercauteren",
}

The NiftyNet platform originated in software developed for Li, et al. 2017:

Licensing and Copyright

NiftyNet is released under the Apache License, Version 2.0.

Copyright 2018 the NiftyNet Consortium.

Acknowledgements

This project is grateful for the support from the Wellcome Trust, the Engineering and Physical Sciences Research Council (EPSRC), the National Institute for Health Research (NIHR), the Department of Health (DoH), Cancer Research UK, King's College London (KCL), University College London (UCL), the Science and Engineering South Consortium (SES), the STFC Rutherford-Appleton Laboratory, and NVIDIA.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].