Unsupervised 3D End-to-end Deformable Network for Brain MRI Registration

Zhu, Zhenyu; Cao, Yiqin; Qin, Chenchen; Rao, Yi; Ni, Dong; Wang, Yi

doi:10.1109/embc44109.2020.9176475

Cited by 10 publications

(6 citation statements)

References 14 publications

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“…There is some research on MRI registration using a deep learning algorithm, which may achieve better performance than traditional MRI registration methods. In the next step, we need to compare and analyze the registration results of these methods ( 38 , 39 ).…”

Section: Discussionmentioning

confidence: 99%

Digital Analysis of Smart Registration Methods for Magnetic Resonance Images in Public Healthcare

Chen

Yuan²,

Tang³

et al. 2022

Front. Public Health

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Brain development and atrophy accompany people's life. Brain development diseases, such as autism and Alzheimer's disease, affect a large part of the population. Analyzing brain development is very important in public healthcare, and image registration is essential in medical brain image analysis. Many previous studies investigate registration accuracy by the “ground truth” dataset, marker-based similarity calculation, and expert check to find the best registration algorithms. But the evaluation of image registration technology only at the accuracy level is not comprehensive. Here, we compare the performance of three publicly available registration techniques in brain magnetic resonance imaging (MRI) analysis based on some key features widely used in previous MRI studies for classification and detection tasks. According to the analysis results, SPM12 has a stable speed and success rate, and it always works as a guiding tool for newcomers to medical image analysis. It can preserve maximum contrast information, which will facilitate studies such as tumor diagnosis. FSL is a mature and widely applicable toolkit for users, with a relatively stable success rate and good performance. It has complete functions and its function-based integrated toolbox can meet the requirements of different researchers. AFNI is a flexible and complex tool that is more suitable for professional researchers. It retains most details in medical image analysis, which makes it useful in fine-grained analysis such as volume estimation. Our study provides a new idea for comparing registration tools, where tool selection strategy mainly depends on the research task in which the selected tool can leverage its unique advantages.

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Section: Discussionmentioning

confidence: 99%

Digital Analysis of Smart Registration Methods for Magnetic Resonance Images in Public Healthcare

Chen

Yuan²,

Tang³

et al. 2022

Front. Public Health

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“…They further learned a meaningful metric for effective training of the registration network, using the discrimination network. Using a similar smooth loss, Zhu et al [51] designed an end-to-end network comprising affine alignment subnetwork and deformable subnetwork, which did not require an additional preprocessing of affine registration before registration. Similarly, Fu et al [52] proposed a LungRegNet based on two GAN-based networks to register lung CT images from coarse to fine, where the adversarial network in GANs was used to enforce additional DVF regularisation.…”

Section: Smoothness Regularisermentioning

confidence: 99%

Deep learning in medical image registration

et al. 2020

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Image registration is a fundamental task in multiple medical image analysis applications. With the advent of deep learning, there have been significant advances in algorithmic performance for various computer vision tasks in recent years, including medical image registration. The last couple of years have seen a dramatic increase in the development of deep learning-based medical image registration algorithms. Consequently, a comprehensive review of the current state-of-the-art algorithms in the field is timely, and necessary. This review is aimed at understanding the clinical applications and challenges that drove this innovation, analysing the functionality and limitations of existing approaches, and at providing insights to open challenges and as yet unmet clinical needs that could shape future research directions. To this end, the main contributions of this paper are: (a) discussion of all deep learning-based medical image registration papers published since 2013 with significant methodological and/or functional contributions to the field; (b) analysis of the development and evolution of deep learning-based image registration methods, summarising the current trends and challenges in the domain; and (c) overview of unmet clinical needs and potential directions for future research in deep learning-based medical image registration.

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“…However, above methods require extra affine preregistration before their proposed deformable registration. To address this issue, some pyramidal and recursive models [17][18][19][20][21] have been proposed to estimate both the affine and deformable mapping from multiresolution or multistage feature maps. Unfortunately, similar to most unsupervised approaches, 15,16,22,23 these networks still only employed intensity-based similarity to optimize registration, but ignored the shape information for the constraint of registration.…”

Section: Introductionmentioning

confidence: 99%

Salient deformable network for abdominal multiorgan registration

2022

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Background: Image registration has long been an active research area in the society of medical image computing, which is to perform spatial transformation between a pair of images and establish a point-wise correspondence to achieve spatial consistency. Purpose: Previous work mainly focused on learning complicated deformation fields by maximizing the global-level (i.e., foreground plus background) image similarity.We argue that taking the background similarity into account may not be a good solution, if we only seek the accurate alignment of target organs/regions in real clinical practice. Methods: We, therefore, propose a novel concept of Salient Registration and introduce a novel deformable network equipped with a saliency module. Specifically, a multitask learning-based saliency module is proposed to discriminate the salient regions-of -registration in a semisupervised manner. Then, our deformable network analyzes the intensity and anatomical similarity of salient regions, and finally conducts the salient deformable registration. Results: We evaluate the efficacy of the proposed network on challenging abdominal multiorgan CT scans. The experimental results demonstrate that the proposed registration network outperforms other state-of -the-art methods, achieving a mean Dice similarity coefficient (DSC) of 40.2%,Hausdorff distance (95 HD) of 20.8 mm, and average symmetric surface distance (ASSD) of 4.58 mm. Moreover, even by training using one labeled data, our network can still attain satisfactory registration performance, with a mean DSC of 39.2%, 95 HD of 21.2 mm, and ASSD of 4.78 mm. Conclusions:The proposed network provides an accurate solution for multiorgan registration and has the potential to be used for improving other registration applications. The code is publicly available at https://github.com/Rrrfrr/Salient-Deformable-Network.

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Unsupervised 3D End-to-end Deformable Network for Brain MRI Registration

Cited by 10 publications

References 14 publications

Digital Analysis of Smart Registration Methods for Magnetic Resonance Images in Public Healthcare

Digital Analysis of Smart Registration Methods for Magnetic Resonance Images in Public Healthcare

Deep learning in medical image registration

Salient deformable network for abdominal multiorgan registration

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