Decoupled learning for brain image registration.
Decoupled learning for brain image registration.
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DOI：
10.3389/fnins.2023.1246769
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发表时间：
2023
影响因子：
4.3
通讯作者：
Wen, Zhijie
中科院分区：
文献类型：
--
作者：
Fang, Jinwu;Lv, Na;Li, Jia;Zhang, Hao;Wen, Jiayuan;Yang, Wan;Wu, Jingfei;Wen, Zhijie
图像配准是医学图像处理和智能分析的重要组成部分。图像配准的准确性将在很大程度上影响后续的图像处理和分析。本文重点研究基于深度学习的脑图像配准问题，提出了基于模型解耦和正则化学习的无监督深度学习方法。具体来说，我们首先将大脑图像配准的高度病态逆问题分解为两个简单的子问题，以降低模型的复杂性。进一步构造了两个轻型神经网络来逼近这两个子问题的解，并采用交替迭代的训练策略来求解问题。利用模型解耦算法的性能进行评估，通过实验进行脑MRI图像从LPBA40数据集。实验结果表明，该算法优于传统的学习方法的背景下，脑图像配准任务。
Image registration is one of the important parts in medical image processing and intelligent analysis. The accuracy of image registration will greatly affect the subsequent image processing and analysis. This paper focuses on the problem of brain image registration based on deep learning, and proposes the unsupervised deep learning methods based on model decoupling and regularization learning. Specifically, we first decompose the highly ill-conditioned inverse problem of brain image registration into two simpler sub-problems, to reduce the model complexity. Further, two light neural networks are constructed to approximate the solution of the two sub-problems and the training strategy of alternating iteration is used to solve the problem. The performance of algorithms utilizing model decoupling is evaluated through experiments conducted on brain MRI images from the LPBA40 dataset. The obtained experimental results demonstrate the superiority of the proposed algorithm over conventional learning methods in the context of brain image registration tasks.
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影响因子：
5.7
作者：
Shattuck, David W.;Mirza, Mubeena;Toga, Arthur W.
通讯作者：
Toga, Arthur W.
DOI：
10.1023/b:visi.0000043755.93987.aa
发表时间：
2005-02-01
影响因子：
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作者：
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影响因子：
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作者：
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影响因子：
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