NiftyNet: a deep-learning platform for medical imaging.
NiftyNet: a deep-learning platform for medical imaging.
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NiftyNet：医学成像深度学习平台。
DOI：
10.1016/j.cmpb.2018.01.025
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发表时间：
2018-05
影响因子：
6.1
通讯作者：
Vercauteren T
中科院分区：
文献类型：
--
作者：
Gibson E;Li W;Sudre C;Fidon L;Shakir DI;Wang G;Eaton-Rosen Z;Gray R;Doel T;Hu Y;Whyntie T;Nachev P;Modat M;Barratt DC;Ourselin S;Cardoso MJ;Vercauteren T
基于TensorFlow API实现了一个用于医学影像领域深度学习的开源平台。典型医学成像机器学习流水线的模块化实现有助于(1)从已建立的预训练网络开始，(2)使现有的神经网络结构适应新问题，以及(3)新解决方案的快速原型。三个深度学习应用程序，包括分割、回归、图像生成和表示学习，作为具体例子说明了该平台的关键功能。医学图像分析和计算机辅助干预问题越来越多地被基于深度学习的解决方案所解决。已建立的深度学习平台是灵活的，但不提供医学图像分析的具体功能，使其适应这一应用领域需要大量的执行工作。因此，在许多研究小组之间出现了大量重复工作和不兼容的基础设施。这项工作介绍了用于医学影像深度学习的开源NiftyNet平台。NiftyNet的目标是加速和简化这些解决方案的开发，并提供一个共同的机制来传播研究成果，供社区使用、调整和建立。NiftyNet基础设施为一系列医学成像应用提供了模块化的深度学习管道，包括分割、回归、图像生成和表示学习应用。NiftyNet管道的组件，包括数据加载、数据增强、网络架构、损失函数和评估指标，都是针对并利用医学图像分析和计算机辅助干预的特点而量身定做的。NiftyNet构建在TensorFlow框架上，默认支持TensorBoard的2D和3D图像可视化以及计算图形等功能。我们提供了三个使用NiftyNet基础设施构建的说明性医学图像分析应用程序：(1)从计算机断层扫描中分割多个腹部器官；(2)图像回归，以从脑磁共振图像预测计算机断层扫描衰减图；以及(3)生成特定解剖姿势的模拟超声图像。NiftyNet基础设施使研究人员能够为分割、回归、图像生成和表示学习应用程序快速开发和分发深度学习解决方案，或将平台扩展到新应用程序。
An open-source platform is implemented based on TensorFlow APIs for deep learning in medical imaging domain. A modular implementation of the typical medical imaging machine learning pipeline facilitates (1) warm starts with established pre-trained networks, (2) adapting existing neural network architectures to new problems, and (3) rapid prototyping of new solutions. Three deep-learning applications, including segmentation, regression, image generation and representation learning, are presented as concrete examples illustrating the platform’s key features. Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. The NiftyNet infrastructure provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on the TensorFlow framework and supports features such as TensorBoard visualization of 2D and 3D images and computational graphs by default. We present three illustrative medical image analysis applications built using NiftyNet infrastructure: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. The NiftyNet infrastructure enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.
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影响因子：
6.1
作者：
Doel T;Shakir DI;Pratt R;Aertsen M;Moggridge J;Bellon E;David AL;Deprest J;Vercauteren T;Ourselin S
通讯作者：
Ourselin S
影响因子：
10.6
作者：
Burgos, Ninon;Cardoso, M. Jorge;Ourselin, Sebastien
通讯作者：
Ourselin, Sebastien
影响因子：
5.7
作者：
Dale, AM;Fischl, B;Sereno, MI
通讯作者：
Sereno, MI
影响因子：
5.7
作者：
Avants BB;Tustison NJ;Song G;Cook PA;Klein A;Gee JC
通讯作者：
Gee JC
DOI：
10.1007/s11548-014-1118-5
发表时间：
2015-07
影响因子：
3
作者：
Johnsen, Stian F.;Taylor, Zeike A.;Clarkson, Matthew J.;Hipwell, John;Modat, Marc;Eiben, Bjoern;Han, Lianghao;Hu, Yipeng;Mertzanidou, Thomy;Hawkes, David J.;Ourselin, Sebastien
通讯作者：
Ourselin, Sebastien