Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning.
Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning.
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DOI：
10.1109/tbme.2015.2496253
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发表时间：
2016-07
期刊：
影响因子：
--
通讯作者：
Shen D
中科院分区：
文献类型：
--
作者：
Wu G;Kim M;Wang Q;Munsell BC;Shen D
特征选择是可变形图像配准的关键步骤。特别是，选择准确而简明地描述图像块中复杂形态模式的最具区分性的特征改进了对应检测，这反过来又提高了图像配准的精度。此外，由于越来越多的成像模式被发明来更好地识别医学成像数据中的形态变化，因此可变形图像配准方法的发展将对医学图像分析社区产生重大影响，该方法能够很好地适应新的图像模式或新的图像应用，而不需要人工干预。为了解决这些问题，提出了一种基于学习的图像配准框架，该框架使用深度学习来发现观测到的成像数据上紧凑且具有高度区分性的特征。具体地说，提出的特征选择方法使用卷积堆叠自动编码器来识别图像块中固有的深层特征表示。由于深度学习是一种无监督的学习方法，因此不需要地面真理标签知识。这使得所提出的特征选择方法对于新的成像模式更加灵活，因为可以在非常短的时间内从观测的成像数据中直接学习特征表示。使用LONI和ADNI成像数据集，图像配准性能与现有的两种使用手工特征的最先进的可变形图像配准方法进行了比较。为了验证所提出的图像配准框架的可扩展性，在7.0特斯拉脑部磁共振图像上进行了图像配准实验。在所有实验中，结果表明，与最先进的图像配准框架相比，新的图像配准框架一致地显示出更准确的配准结果。
Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data,, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked auto-encoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework image registration experiments were conducted on 7.0-tesla brain MR images. In all experiments, the results showed the new image registration framework consistently demonstrated more accurate registration results when compared to state-of-the-art.