Deep learning in medical image registration: a survey

Haskins, Grant; Krüger, Uwe; Yan, Pingkun

doi:10.1007/s00138-020-01060-x

Cited by 458 publications

(325 citation statements)

References 121 publications

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“…However, deep learning in medical image registration has not been extensively studied until the past three to four years. Though several review papers on deep learning in medical image analysis have been published [73,93,96,105,106,121,132,182], there are very few review papers that are specific to deep learning in medical image registration [60]. The goal of this paper is to summarize the latest developments, challenges and trends in DL-based medical image registration methods.…”

Section: Introductionmentioning

confidence: 99%

Deep learning in medical image registration: a review

et al. 2020

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This paper presents a review of deep learning (DL) based medical image registration methods. We summarized the latest developments and applications of DL-based registration methods in the medical field. These methods were classified into seven categories according to their methods, functions and popularity. A detailed review of each category was presented, highlighting important contributions and identifying specific challenges. A short assessment was presented following the detailed review of each category to summarize its achievements and future potentials. We provided a comprehensive comparison among DL-based methods for lung and brain registration using benchmark datasets. Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of DL-based medical image registration. 1 arXiv:1912.12318v1 [eess.IV] 27 Dec 2019 1. Summarize the latest developments in DL-based medical image registration. 2. Highlight contributions, identify challenges and outline future trends. 3. Provide detailed statistics on recent publications from different perspectives. 2 Deep Learning 2 Convolutional Neural NetworkConvolutional neural network (CNN) is a class of deep neural networks with regularized multilayer perceptron. CNN uses convolution operation in place of general matrix multiplication in simple neural networks. The convolutional filters and operations in CNN make it suitable for visual imagery signal processing. Because of its excellent feature extraction ability, CNN is one of the most successful models for image analysis. Since the breakthrough of AlexNet [79], many variants of CNN have been proposed and have achieved the-state-of-art performances in various image processing tasks. A typical CNN usually consists of multiple convolutional layers, max pooling layers, batch normalization layers, dropout layers, a sigmoid or softmax layer. In each convolutional layer, multiple channels of feature maps were extracted by sliding trainable convolutional kernels across the input feature maps. Hierarchical features with high-level abstraction are extracted using multiple convolutional layers. These feature maps usually go through multiple fully connected layer before reaching the final decision layer. Max pooling layers are often used to reduce the image sizes and to promote spatial invariance of the network. Batch normalization is used to reduce internal covariate shift among the training samples. Weight regularization and dropout layers are used to alleviate data overfitting. The loss function is defined as the difference between the predicted and the target output. CNN is usually trained by minimizing the loss via gradient back propagation using optimization methods. Many different types of network architectures have been proposed to improve the performance of CNN [93]. U-Net proposed by Ronneberger et al. is among one of the most used network architectures [120]. U-Net was originally used to perform neuronal structures segmentation. U-Net adopts symmetrical contractiv...

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Section: Introductionmentioning

confidence: 99%

Deep learning in medical image registration: a review

et al. 2020

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“…Finally, motion features can boost segmentation performances (15)(16)(17), as looking at several frames improves the manual segmentation of physicians. Further details are given in a review dedicated to deep learning for motion estimation in medical imaging (18).…”

Section: Motion and Deformation Estimationmentioning

confidence: 99%

Machine Learning Approaches for Myocardial Motion and Deformation Analysis

Duchateau

King

Craene

2020

Front. Cardiovasc. Med.

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Information about myocardial motion and deformation is key to differentiate normal and abnormal conditions. With the advent of approaches relying on data rather than preconceived models, machine learning could either improve the robustness of motion quantification or reveal patterns of motion and deformation (rather than single parameters) that differentiate pathologies. We review machine learning strategies for extracting motion-related descriptors and analyzing such features among populations, keeping in mind constraints specific to the cardiac application.

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“…Image registration aims to transform different images into one system with the matched imaging contents, which has significant applications in brain image analysis, including brain atlas creation [3], tumor growth monitoring [7] and multi-modality image fusion [5]. When we analyze a pair of brain images that were acquired from different sensors and viewpoints at different times, we need to transform one image (unaligned image) to another image (reference image) by establishing the anatomical correspondences [4,6,13].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration

Liu

Zhu

et al. 2019

Lecture Notes in Computer Science

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Brain image registration transforms a pair of images into one system with the matched imaging contents, which is of essential importance for brain image analysis. This paper presents a novel framework for unsupervised 3D brain image registration by capturing the feature-level transformation relationships between the unaligned image and reference image. To achieve this, we develop a feature-level probabilistic model to provide the direct regularization to the hidden layers of two deep convolutional neural networks, which are constructed from two input images. This model design is developed into multiple layers of these two networks to capture the transformation relationships at different levels. We employ two common benchmark datasets for 3D brain image registration and perform various experiments to evaluate our method. Experimental results show that our method clearly outperforms state-of-the-art methods on both benchmark datasets by a large margin.

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Deep learning in medical image registration: a survey

Cited by 458 publications

References 121 publications

Deep learning in medical image registration: a review

Deep learning in medical image registration: a review

Machine Learning Approaches for Myocardial Motion and Deformation Analysis

Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration

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