Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions.
Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions.
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DOI：
10.1007/s10278-017-9983-4
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发表时间：
2017-08
影响因子：
4.4
通讯作者：
Erickson BJ
中科院分区：
文献类型：
--
作者：
Akkus Z;Galimzianova A;Hoogi A;Rubin DL;Erickson BJ
脑MRI的定量分析对于许多神经系统疾病和病症是常规的，并且依赖于感兴趣结构的准确分割。基于深度学习的脑MRI分割方法由于其在大量数据上的自学习和泛化能力而越来越受到关注。随着深度学习架构变得越来越成熟，它们逐渐超越了以前最先进的经典机器学习算法。本文旨在概述当前基于深度学习的定量脑MRI分割方法。首先，我们回顾了当前用于分割解剖大脑结构和大脑病变的深度学习架构。接下来，总结和讨论深度学习方法的性能、速度和属性。最后，我们对当前状态进行了批判性评估，并确定了未来可能的发展和趋势。
Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends.
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