TransMorph: Transformer for unsupervised medical image registration.
TransMorph: Transformer for unsupervised medical image registration.
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DOI：
10.1016/j.media.2022.102615
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发表时间：
2022-11
影响因子：
10.9
通讯作者：
Du, Yong
中科院分区：
文献类型：
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作者：
Chen, Junyu;Frey, Eric C.;He, Yufan;Segars, William P.;Li, Ye;Du, Yong
在过去的十年中，卷积神经网络（ConvNets）一直是医学图像分析研究的主要焦点。然而，ConvNets的性能可能会受到缺乏对图像中的远程空间关系的明确考虑的限制。最近，Vision Transformer架构被提出来解决ConvNets的缺点，并在许多医学成像应用中产生了最先进的性能。变压器可能是一个强有力的候选人的图像配准，因为他们的实质上更大的感受野，使运动和固定图像之间的空间对应关系的更精确的理解。在这里，我们提出了transMorph，一个混合的transformer-ConvNet模型的体积医学图像配准。本文还介绍了transMorph的非纯变体和贝叶斯变体：非纯变体确保拓扑保持变形，贝叶斯变体产生校准良好的配准不确定性估计。我们广泛验证了所提出的模型，使用三维医学图像从三个应用程序：患者间和图集到患者的大脑MRI配准和幻影到CT配准。所提出的模型进行评估，比较各种现有的注册方法和Transformer架构。定性和定量的结果表明，所提出的基于变压器的模型导致了一个显着的性能改善的基线方法，确认变压器的有效性的医学图像配准。
In the last decade, convolutional neural networks (ConvNets) have been a major focus of research in medical image analysis. However, the performances of ConvNets may be limited by a lack of explicit consideration of the long-range spatial relationships in an image. Recently Vision Transformer architectures have been proposed to address the shortcomings of ConvNets and have produced state-of-the-art performances in many medical imaging applications. Transformers may be a strong candidate for image registration because their substantially larger receptive field enables a more precise comprehension of the spatial correspondence between moving and fixed images. Here, we present TransMorph, a hybrid Transformer-ConvNet model for volumetric medical image registration. This paper also presents diffeomorphic and Bayesian variants of TransMorph: the diffeomorphic variants ensure the topology-preserving deformations, and the Bayesian variant produces a well-calibrated registration uncertainty estimate. We extensively validated the proposed models using 3D medical images from three applications: inter-patient and atlas-to-patient brain MRI registration and phantom-to-CT registration. The proposed models are evaluated in comparison to a variety of existing registration methods and Transformer architectures. Qualitative and quantitative results demonstrate that the proposed Transformer-based model leads to a substantial performance improvement over the baseline methods, confirming the effectiveness of Transformers for medical image registration.
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DOI：
10.1007/978-3-642-23623-5_69
发表时间：
2011
期刊：
LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
影响因子：
--
作者：
Risholm, Petter;Balter, James;Wells, William M.
通讯作者：
Wells, William M.
DOI：
10.1023/b:visi.0000043755.93987.aa
发表时间：
2005-02-01
影响因子：
19.5
作者：
Beg, MF;Miller, MI;Younes, L
通讯作者：
Younes, L
影响因子：
10.9
作者：
de Vos, Bob D.;Berendsen, Floris F.;Isgum, Ivana
通讯作者：
Isgum, Ivana
影响因子：
3.4
作者：
Devalla, Sripad Krishna;Renukanand, Prajwal K.;Girard, Michael J. A.
通讯作者：
Girard, Michael J. A.
使用完全无监督的可变形图像与卷积神经网络配准来生成拟人化模型。
影响因子：
3.8
作者：
通讯作者：
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