A review of deep learning-based deformable medical image registration

Zou, Jing; Gao, Bingchen; Song, Youyi; Qin, Jing

doi:10.3389/fonc.2022.1047215

Cited by 36 publications

(18 citation statements)

References 146 publications

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“…Recent progress in the field of deep learning may be an approach capable of mitigating the co-registration errors. 23…”

Section: Discussionmentioning

confidence: 99%

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MRI‐based quantification of carbon and oxygen concentrations in human soft tissues for range verification in proton therapy

Saito

2023

Medical Physics

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BackgroundIn‐situ range verification of particle therapy based on the detection of secondary emitted radiation requires highly accurate assignment of elemental concentrations (particularly carbon and oxygen) in the human body.PurposeA method for quantitatively predicting carbon and oxygen concentrations in human soft tissues is proposed. This method relies on an empirical one‐to‐one correspondence between the mass fraction and water content (WC), which is a measurable tissue quantity based on magnetic resonance (MR) imaging (referred to as “MRWC‐based method”).MethodsA numerical analysis of the MRWC‐based method was performed for 47 standard human soft tissues tabulated in the literature as objects of interest with unknown mass fractions of the four main elements—C, O, H, and N. Thereafter, the method was evaluated in terms of the mass‐fraction quantification accuracy by comparing it with the gold‐standard CT‐based method developed by Schneider et al. The MRWC‐based method was also applied to the MR imaging data of a virtual head phantom obtained from a three‐dimensional MRI‐simulated brain database.ResultsThe predicted mass fractions in a range of human soft tissues were in better agreement with the reference values than those predicted by the CT‐based method. The mean absolute errors of the predicted mass% values for the overall standard soft tissues could be reduced from 4.8 percentage points (pp) (CT‐based) to 0.5 pp (MRWC‐based) for carbon and from 5.2 pp (CT‐based) to 0.4 pp (MRWC‐based) for oxygen. The application to the simulated MRI data confirmed the capability of the sufficient recognition of the boundaries between the white matter and gray matter in the brain that could not be realized by the CT‐based method. Thus, the MRWC‐based method exhibits superior performance in the prediction of carbon and oxygen concentrations in soft tissues.ConclusionsThis study is limited to a proof‐of‐concept scope but demonstrates the feasibility of the MRWC‐based method for the generation of elemental images of human soft tissues from MRI‐derived water‐content images.

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“…Recent progress in the field of deep learning may be an approach capable of mitigating the co-registration errors. 23…”

Section: Discussionmentioning

confidence: 99%

“…Thus, precise co‐registration is crucial to take full advantage of the MRWC‐based method. Recent progress in the field of deep learning may be an approach capable of mitigating the co‐registration errors 23 …”

Section: Discussionmentioning

confidence: 99%

MRI‐based quantification of carbon and oxygen concentrations in human soft tissues for range verification in proton therapy

Saito

2023

Medical Physics

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“…Compared with traditional medical image registration methods, the greatest contribution of DL in medical image registration is to resolve the problem of slow medical image processing. 27 Eppenhof and Pluim 29 studied a CNN-based deformable registration algorithm and compared it with traditional algorithms. Their results showed that the registration speed of DL networks is hundreds of times that of traditional registration methods, with an average of 0.58 + 0.07s.…”

Section: Implementation Areamentioning

confidence: 99%

“…In recent years, DL, especially CNN, has achieved good results in medical image processing, and medical registration research has developed rapidly. 27 The 2 main types of existing medical image registration methods are gray-scale-based methods and featurebased methods. The primary steps of image registration include geometric size change, combined image change, image similarity measurement, iterative optimization, and interpolation process.…”

Section: Implementation Areamentioning

confidence: 99%

The Application and Development of Deep Learning in Radiotherapy: A Systematic Review

Huang

Bai

Wang

et al. 2021

Technol Cancer Res Treat

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With the massive use of computers, the growth and explosion of data has greatly promoted the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such as convolutional neural networks (CNN), has provided radiation oncologists with many promising tools that can simplify the complex radiotherapy process in the clinical work of radiation oncology, improve the accuracy and objectivity of diagnosis, and reduce the workload, thus enabling clinicians to spend more time on advanced decision-making tasks. As the development of DL gets closer to clinical practice, radiation oncologists will need to be more familiar with its principles to properly evaluate and use this powerful tool. In this paper, we explain the development and basic concepts of AI and discuss its application in radiation oncology based on different task categories of DL algorithms. This work clarifies the possibility of further development of DL in radiation oncology.

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“…neuro-/radiation oncology [ 5 ]. Their capability for the deformable registration task has been increasingly investigated in different medical image modalities [ 6 ]. In these methods, in contrast to classical ones, deformable registration is defined as a parametric function.…”

Section: Introductionmentioning

confidence: 99%

Deformable registration of magnetic resonance images using unsupervised deep learning in neuro-/radiation oncology

Osman,

Al-Mugren,

Tamam

et al. 2024

Radiat Oncol

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Purpose Accurate deformable registration of magnetic resonance imaging (MRI) scans containing pathologies is challenging due to changes in tissue appearance. In this paper, we developed a novel automated three-dimensional (3D) convolutional U-Net based deformable image registration (ConvUNet-DIR) method using unsupervised learning to establish correspondence between baseline pre-operative and follow-up MRI scans of patients with brain glioma. Methods This study involved multi-parametric brain MRI scans (T1, T1-contrast enhanced, T2, FLAIR) acquired at pre-operative and follow-up time for 160 patients diagnosed with glioma, representing the BraTS-Reg 2022 challenge dataset. ConvUNet-DIR, a deep learning-based deformable registration workflow using 3D U-Net style architecture as a core, was developed to establish correspondence between the MRI scans. The workflow consists of three components: (1) the U-Net learns features from pairs of MRI scans and estimates a mapping between them, (2) the grid generator computes the sampling grid based on the derived transformation parameters, and (3) the spatial transformation layer generates a warped image by applying the sampling operation using interpolation. A similarity measure was used as a loss function for the network with a regularization parameter limiting the deformation. The model was trained via unsupervised learning using pairs of MRI scans on a training data set (n = 102) and validated on a validation data set (n = 26) to assess its generalizability. Its performance was evaluated on a test set (n = 32) by computing the Dice score and structural similarity index (SSIM) quantitative metrics. The model’s performance also was compared with the baseline state-of-the-art VoxelMorph (VM1 and VM2) learning-based algorithms. Results The ConvUNet-DIR model showed promising competency in performing accurate 3D deformable registration. It achieved a mean Dice score of 0.975 ± 0.003 and SSIM of 0.908 ± 0.011 on the test set (n = 32). Experimental results also demonstrated that ConvUNet-DIR outperformed the VoxelMorph algorithms concerning Dice (VM1: 0.969 ± 0.006 and VM2: 0.957 ± 0.008) and SSIM (VM1: 0.893 ± 0.012 and VM2: 0.857 ± 0.017) metrics. The time required to perform a registration for a pair of MRI scans is about 1 s on the CPU. Conclusions The developed deep learning-based model can perform an end-to-end deformable registration of a pair of 3D MRI scans for glioma patients without human intervention. The model could provide accurate, efficient, and robust deformable registration without needing pre-alignment and labeling. It outperformed the state-of-the-art VoxelMorph learning-based deformable registration algorithms and other supervised/unsupervised deep learning-based methods reported in the literature.

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A review of deep learning-based deformable medical image registration

Cited by 36 publications

References 146 publications

MRI‐based quantification of carbon and oxygen concentrations in human soft tissues for range verification in proton therapy

MRI‐based quantification of carbon and oxygen concentrations in human soft tissues for range verification in proton therapy

The Application and Development of Deep Learning in Radiotherapy: A Systematic Review

Deformable registration of magnetic resonance images using unsupervised deep learning in neuro-/radiation oncology

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