【推荐】NiftyNet：面向医学图像分析和图像引导治疗的开源CNN平台（附代码）

                                                                                                                                                                                      

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摘要

 

转自：爱可可-爱生活

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.

Loss Functions

Publications relating to the various loss functions used in the NiftyNet framework can be found listed below.

• Dice Loss (Milletari et. al. 2016)

• Generalised Dice Loss (Sudre et. al 2017)

• Sensitivity-Specifity Loss (Brosch et. al. MICCAI 2015)

• Wasserstein Dice Loss (Fidon et. al. 2017)

链接：

http://www.niftynet.io/

github链接：

https://github.com/NifTK/NiftyNet

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原文链接：

https://m.weibo.cn/1402400261/4200669231413217

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