Medical Image Registration Method Research Articles

Broad-UNet-diff: Diffeomorphic Deformable Medical Image Registration based on Multi-scale Feature Learning

Introduction: To propose a medical image registration method with significant performance improvement. The spatial transformation obtained by the traditional deformable image registration technology is not smooth enough, and the calculation amount is too large to solve the optimization parameters. The network model proposed based on deep learning medical image registration technology has some limitations, which cannot guarantee the registration of topological structures, resulting in the loss of spatial features. It makes the model have topological conservation and transform reversibility, has the ability to learn more multi-scale features and complex image structures, and captures finer changes while clearly encoding global correlation. Method: Based on the core UNet model, a deformable image registration method with a new architecture Broad-UNet-diff is proposed. The model is equipped with asymmetric parallel convolution and uses diffeomorphism mapping. Result: Compared with the seven classical registration methods under the brain MRI datasets, the proposed method has significantly improved the registration performance. In particular, compared with the advanced TransMorph-diff registration method, the Dice score can be improved by 12 %, but only the 1/10 parameters are needed. Conclusion: This method confirms its convincing effectiveness and accuracy.

QUIZ: An arbitrary volumetric point matching method for medical image registration

Rigid pre-registration involving local–global matching or other large deformation scenarios is crucial. Current popular methods rely on unsupervised learning based on grayscale similarity, but under circumstances where different poses lead to varying tissue structures, or where image quality is poor, these methods tend to exhibit instability and inaccuracies. In this study, we propose a novel method for medical image registration based on arbitrary voxel point of interest matching, called query point quizzer (QUIZ). QUIZ focuses on the correspondence between local–global matching points, specifically employing CNN for feature extraction and utilizing the Transformer architecture for global point matching queries, followed by applying average displacement for local image rigid transformation.We have validated this approach on a large deformation dataset of cervical cancer patients, with results indicating substantially smaller deviations compared to state-of-the-art methods. Remarkably, even for cross-modality subjects, it achieves results surpassing the current state-of-the-art.

MacJNet: weakly-supervised multimodal image deformable registration using joint learning framework and multi-sampling cascaded MIND

Deformable multimodal image registration plays a key role in medical image analysis. It remains a challenge to find accurate dense correspondences between multimodal images due to the significant intensity distortion and the large deformation. macJNet is proposed to align the multimodal medical images, which is a weakly-supervised multimodal image deformable registration method using a joint learning framework and multi-sampling cascaded modality independent neighborhood descriptor (macMIND). The joint learning framework consists of a multimodal image registration network and two segmentation networks. The proposed macMIND is a modality-independent image structure descriptor to provide dense correspondence for registration, which incorporates multi-orientation and multi-scale sampling patterns to build self-similarity context. It greatly enhances the representation ability of cross-modal features in the registration network. The semi-supervised segmentation networks generate anatomical labels to provide semantics correspondence for registration, and the registration network helps to improve the performance of multimodal image segmentation by providing the consistency of anatomical labels. 3D CT-MR liver image dataset with 118 samples is built for evaluation, and comprehensive experiments have been conducted to demonstrate that macJNet achieves superior performance over state-of-the-art multi-modality medical image registration methods.

A review of deep learning-based deformable medical image registration.

The alignment of images through deformable image registration is vital to clinical applications (e.g., atlas creation, image fusion, and tumor targeting in image-guided navigation systems) and is still a challenging problem. Recent progress in the field of deep learning has significantly advanced the performance of medical image registration. In this review, we present a comprehensive survey on deep learning-based deformable medical image registration methods. These methods are classified into five categories: Deep Iterative Methods, Supervised Methods, Unsupervised Methods, Weakly Supervised Methods, and Latest Methods. A detailed review of each category is provided with discussions about contributions, tasks, and inadequacies. We also provide statistical analysis for the selected papers from the point of view of image modality, the region of interest (ROI), evaluation metrics, and method categories. In addition, we summarize 33 publicly available datasets that are used for benchmarking the registration algorithms. Finally, the remaining challenges, future directions, and potential trends are discussed in our review.

Cascaded multi-level medical image registration method based on transformer

In deep learning-based image registration, the deformable region with complex anatomical structures is an important factor affecting the accuracy of network registration. However, it is difficult for existing methods to pay attention to complex anatomical regions of images. At the same time, the receptive field of the convolutional neural network is limited by the size of its convolution kernel, and it is difficult to learn the relationship between the voxels with far spatial location, making it difficult to deal with the large region deformation problem. Aiming at the above two problems, this paper proposes a cascaded multi-level registration network model based on transformer, and equipped it with a difficult deformable region perceptron based on mean square error. The difficult deformation perceptron uses sliding window and floating window techniques to retrieve the registered images, obtain the difficult deformation coefficient of each voxel, and identify the regions with the worst registration effect. In this study, the cascaded multi-level registration network model adopts the difficult deformation perceptron for hierarchical connection, and the self-attention mechanism is used to extract global features in the basic registration network to optimize the registration results of different scales. The experimental results show that the method proposed in this paper can perform progressive registration of complex deformation regions, thereby optimizing the registration results of brain medical images, which has a good auxiliary effect on the clinical diagnosis of doctors.

An Unsupervised 3D Image Registration Network for Brain MRI Deformable Registration.

In recent years, deep learning has made successful applications and remarkable achievements in the field of medical image registration, and the method of medical image registration based on deep learning has become the current research hotspot. However, the performance of convolutional neural networks may not be fully exploited due to neglect of spatial relationships between distant locations in the image and incomplete updates of network parameters. To avoid this phenomenon, MHNet, a multiscale hierarchical deformable registration network for 3D brain MR images, was proposed in this paper. This network was an unsupervised end-to-end convolutional neural network. After training, the dense displacement vector field can be predicted almost in real-time for the unseen input image pairs, which saves a lot of time compared with the traditional algorithms of independent iterative optimization for each pair of images. On the basis of the encoder-decoder structure, this network introduced the improved Inception module for multiscale feature extraction and expanding the receptive field and the hierarchical forecast structure to promote the update of the parameters of the middle layers, which achieved the best performance on the augmented public dataset compared with the existing four excellent registration methods.

Effect Analysis of Nursing Method Based on Stratified Emergency Knowledge in Emergency Myocardial Infarction.

First aid is to give immediate first aid to patients who have suffered accidental injuries or sudden diseases before the emergency medical personnel arrive at the scene or are sent to the hospital for treatment; myocardial infarction is a severe and emergency of coronary heart disease. It is often because of coronary atherosclerosis, plaque rupture, bleeding, or thrombosis, which leads to the acute and complete occlusion of the coronary arteries and acute necrosis of the myocardium. This article aims to investigate the effectiveness of a stratified emergency care team assisted by multidisciplinary first aid knowledge in acute myocardial infarction first aid, hoping to reduce the probability of acute myocardial infarction through first aid and nursing care. Business process reengineering is a management idea that reached its heyday in the 1990s and is usually defined as achieving workflow and productivity through the reorganization and optimization of corporate strategies, value-added operational processes, and the systems, policies, organizations, and structures that support them. This article first outlines the concepts and steps of medical image registration, analyzes the characteristics of current medical image registration methods, and uses the two most commonly used medical registration methods today; in this study, the BPR theory was used to construct the AMI emergency care process in the hospital, which effectively reduced the emergency delay time of AMI patients, improved the patient's emergency response, and increased the efficiency of emergency nurses' rescue work. The experimental results in this paper show that the sensitivities of the first four groups of ST↑III > II, ST ↓ aVL > I, STV3 ↓ /STIII↑≤1.2, STI ↓ ≥0.05 mV are 82.1%, 80%, 75.3%, and 60.3%, respectively. Their sensitivities are relatively close, both are greater than 50%; among them, ST↑III > II has the highest sensitivity, which is 80%. In terms of specificity, ST↑III > II, ST ↓ aVL > I, STV3 ↓ /STIII↑≤1.2, and STI ↓ ≥0.05 mV were 82.1%, 89.2%, 82.7%, and 65.2%, respectively. ST ↓ ≥0.05 mV has the lowest specificity, and ST ↓ aVL > I has the highest specificity.

Deformable image registration with deep network priors: a study on longitudinal PET images

Objective. This paper proposes a novel approach for the longitudinal registration of PET imaging acquired for the monitoring of patients with metastatic breast cancer. Unlike with other image analysis tasks, the use of deep learning (DL) has not significantly improved the performance of image registration. With this work, we propose a new registration approach to bridge the performance gap between conventional and DL-based methods: medical image registration method regularized by architecture (MIRRBA). Approach. MIRRBA is a subject-specific deformable registration method which relies on a deep pyramidal architecture to parametrize the deformation field. Diverging from the usual deep-learning paradigms, MIRRBA does not require a learning database, but only a pair of images to be registered that is used to optimize the network's parameters. We applied MIRRBA on a private dataset of 110 whole-body PET images of patients with metastatic breast cancer. We used different architecture configurations to produce the deformation field and studied the results obtained. We also compared our method to several standard registration approaches: two conventional iterative registration methods (ANTs and Elastix) and two supervised DL-based models (LapIRN and Voxelmorph). Registration accuracy was evaluated using the Dice score, the target registration error, the average Hausdorff distance and the detection rate, while the realism of the registration obtained was evaluated using Jacobian's determinant. The ability of the different methods to shrink disappearing lesions was also computed with the disappearing rate. Main results. MIRRBA significantly improved all metrics when compared to DL-based approaches. The organ and lesion Dice scores of Voxelmorph improved by 6% and 52% respectively, while the ones of LapIRN increased by 5% and 65%. Regarding conventional approaches, MIRRBA presented comparable results showing the feasibility of our method. Significance. In this paper, we also demonstrate the regularizing power of deep architectures and present new elements to understand the role of the architecture in DL methods used for registration.

Deep residual-SVD network for brain image registration

Objective. Medical image registration aims to find the deformation field that can align two images in a spatial position. A medical image registration method based on U-Net architecture has been proposed currently. However, U-Net architecture has few training parameters, which leads to weak learning ability, and it ignores the adverse effects of image noise on the registration accuracy. The article aims at addressing the problem of weak network learning ability and the adverse effects of noisy images on registration. Approach. Here we propose a novel unsupervised 3D brain image registration framework, which introduces the residual unit and singular value decomposition (SVD) denoising layer on the U-Net architecture. Residual unit solves the problem of network degradation, that is, registration accuracy becomes saturated and then degrades rapidly with the increase in network depth. SVD denoising layer uses the estimated model order for SVD-based low-rank image reconstruction. we use Akaike information criterion to estimate the appropriate model order, which is used to remove noise components. We use the exponential linear unit (ELU) as the activation function, which is more robust to noise than other peers. Main results. The proposed method is evaluated on the publicly available brain MRI datasets: Mindboggle101 and LPBA40. Experimental results demonstrate our method outperforms several state-of-the-art methods for the metric of Dice Score. The mean number of folding voxels and registration time are comparable to state-of-the-art methods. Significance. This study shows that Deep Residual-SVD Network can improve registration accuracy. This study also demonstrate that the residual unit can enhance the learning ability of the network, the SVD denoising layer can denoise effectively, and the ELU is more robust to noise.

Medical Image Registration Algorithm Based on Bounded Generalized Gaussian Mixture Model.

In this paper, a method for medical image registration based on the bounded generalized Gaussian mixture model is proposed. The bounded generalized Gaussian mixture model is used to approach the joint intensity of source medical images. The mixture model is formulated based on a maximum likelihood framework, and is solved by an expectation-maximization algorithm. The registration performance of the proposed approach on different medical images is verified through extensive computer simulations. Empirical findings confirm that the proposed approach is significantly better than other conventional ones.

CoCycleReg: Collaborative cycle-consistency method for multi-modal medical image registration

Multi-modal image registration is an essential step for many medical image analysis applications. Recent advances in multi-modal image registration rely on image-to-image translation to achieve good performance. However, the performance is still limited owing to the poor use of complementary regularization between image registration and translation, which is able to simultaneously enhance both parts’ accuracy. To this end, we propose CoCycleReg, a novel method that formulates image registration and translation in a CollaborativeCycle-consistency manner. Instead of dividing into two discrete stages, we unify the image registration and translation via cycle-consistency in an end-to-end training process, such that each part can benefit from the other one. To ensure the deformation fields’ reversibility in the cycle, we extensively introduce a novel dual-head registration network, consisting of one single backbone to extract the features and two heads to respectively predict the deformation fields. The experiments on T1-T2(MRI) and CT-MRI datasets validate that the proposed CoCycleReg surpasses the other state-of-the-art conventional and deep learning approaches comprehensively considering the speed, accuracy, and regularity of deformation fields. In the ablation analysis, a method that sets the cycle-consistency Corresponding authors at: Department of Computer Science at School of Informatics, Xiamen University, Xiamen 361005, Chinaconstraints of registration and image-to-image translation separately is compared, and the results demonstrate the effectiveness of collaborative cycle-consistency. In addition, the improvement of image-to-image translation is also verified in further analysis. The code is publicly available at https://github.com/DopamineLcy/cocycle-reg/.

Non-rigid Multi-Modal Medical Image Registration Based on Improved Maximum Mutual Information PV Image Interpolation Method.

With the continuous improvement of medical imaging equipment, CT, MRI and PET images can obtain accurate anatomical information of the same patient site. However, due to the fuzziness of medical image physiological evaluation and the unhealthy understanding of objects, the registration effect of many methods is not ideal. Therefore, based on the medical image registration model of Partial Volume (PV) image interpolation method and rigid medical image registration method, this paper established the non-rigid registration model of maximum mutual information Novel Partial Volume (NPV) image interpolation method. The proposed NPV interpolation method uses the Davidon-Fletcher-Powell algorithm (DFP) algorithm optimization method to solve the transformation parameter matrix and realize the accurate transformation of the floating image. In addition, the cubic B-spline is used as the kernel function to improve the image interpolation, which effectively improves the accuracy of the registration image. Finally, the proposed NPV method is compared with the PV interpolation method through the human brain CT-MRI-PET image to obtain a clear CT-MRI-PET image. The results show that the proposed NPV method has higher accuracy, better robustness, and easier realization. The model should also have guiding significance in face recognition and fingerprint recognition.

A review of deep learning-based three-dimensional medical image registration methods.

Medical image registration is a vital component of many medical procedures, such as image-guided radiotherapy (IGRT), as it allows for more accurate dose-delivery and better management of side effects. Recently, the successful implementation of deep learning (DL) in various fields has prompted many research groups to apply DL to three-dimensional (3D) medical image registration. Several of these efforts have led to promising results. This review summarized the progress made in DL-based 3D image registration over the past 5 years and identify existing challenges and potential avenues for further research. The collected studies were statistically analyzed based on the region of interest (ROI), image modality, supervision method, and registration evaluation metrics. The studies were classified into three categories: deep iterative registration, supervised registration, and unsupervised registration. The studies are thoroughly reviewed and their unique contributions are highlighted. A summary is presented following a review of each category of study, discussing its advantages, challenges, and trends. Finally, the common challenges for all categories are discussed, and potential future research topics are identified.

A Medical Image Registration Method Based on Progressive Images.

Background Medical image registration is an essential task for medical image analysis in various applications. In this work, we develop a coarse-to-fine medical image registration method based on progressive images and SURF algorithm (PI-SURF) for higher registration accuracy. Methods As a first step, the reference image and the floating image are fused to generate multiple progressive images. Thereafter, the floating image and progressive image are registered to get the coarse registration result based on the SURF algorithm. For further improvement, the coarse registration result and the reference image are registered to perform fine image registration. The appropriate progressive image has been investigated by experiments. The mutual information (MI), normal mutual information (NMI), normalized correlation coefficient (NCC), and mean square difference (MSD) similarity metrics are used to demonstrate the potential of the PI-SURF method. Results For the unimodal and multimodal registration, the PI-SURF method achieves the best results compared with the mutual information method, Demons method, Demons+B-spline method, and SURF method. The MI, NMI, and NCC of PI-SURF are improved by 15.5%, 1.31%, and 7.3%, respectively, while MSD decreased by 13.2% for the multimodal registration compared with the optimal result of the state-of-the-art methods. Conclusions The extensive experiments show that the proposed PI-SURF method achieves higher quality of registration.

A 3D medical image registration method based on multi-scale feature fusion

Deformable medical image registration refers to finding a certain transformation so that the corresponding points of two medical images can be aligned in space. This has important clinical applications. In this article, we propose an unsupervised end-to-end medical image registration method. In this method, the fixed image and the moving image are concatenated in series and input into the convolution neural network to obtain the feature images of different scales. In order to improve the ability of neural network to capture global and local information, we fuse feature maps of different scales. The spatial transformation network uses the deformation field to deform the moving image, so as to realize the registration of the two pairs. We validate our method in the ABIDE data set and compare it with some classic state-of-the-art methods. The experimental results show that our method improves the registration accuracy of image pairs.

Study on anatomical and functional medical image registration methods

The purpose of this paper is to give an overview of various well known medical image registration techniques with a special focus on registration between anatomical and functional medical images. Examples of anatomical medical images (AMI) are Computer Tomography (CT), Magnetic Resonance Images (MRI), X-ray radiographs and ultrasound etc. whereas Positron Emission Tomography (PET), Single Photon Emission Tomography (SPECT) and fMRI are examples of functional medical images (FMI). Irrespective of such types (AMI or FMI), every case can be considered as a medical imaging modality. All these modalities are widely used by clinicians to study the structure/functionality of human body parts for diagnosis and treatment. It is frequently required to combine PET/SPECT with CT/MRI to simultaneous study the metabolic and molecular information received through PET/SPECT with fine anatomical details observed by CT/MRI. This concurrent study will help in the diagnosis and localization of many diseases like cancer, blockage in coronary arteries and brain-related diseases like Parkinson, Alzheimer etc. Further, in many cases, clinicians are required to co-register one or more anatomical images with functional images, for example, ultrasound-guided biopsy fused with PET and MRI. This registration can be done either at the hardware level or at the software level. The introduction of integrated PET-CT machine increases the acceptability of hardware-based registration systems as compared to software-based methods among the medics. One possible reason for this is the lack of validation of results achieved through software-based registration methods. On the other hand, software-based registration methods also have many advantages over the hardware-based registration systems like lesser exposure to radiation and no need for new investment on hardware etc. In this paper, both the methods with their merits and demerits are discussed in detail.

Application of Algorithm with Novel Successive Approximate Registration Method of Medical Image

Medical image registration is widely used in the clinical field. A simple and effective registration method for medical images is proposed, which is not only used images’ hierarchical information, but also the successive approximate method is used. It mainly includes two steps, which are the coarse registration and the fine registration. In the coarse registration procedure, the couple images’ edge features are extracted. Further more, the contour line features are extracted based on the edge features. The contour lines are selected as registration objects to accomplish the coarse registration based on the Principal Axis method. In the second registration procedure, i.e. the fine registration, the hierarchical information of the images are obtained by the convolution operator and pooling method firstly. Then these information are matched from the last scale to the first scale to obtain the last registration result. The proposed method’s validity is proved by different registration experiments, and the proposed method can also be applied to incomplete images’ registration. The error of registration is less than one pixel unit in the different registration experiments of the proposed method.

Research on Medical Image Registration Based on Graphic Neural Network Reinforcement Learning

On the basis of traditional medical image registration methods, aiming at the shortcomings of rigid body images, sequential rigid body images and non-rigid body images registration methods, this paper proposes a new method of medical image registration application based on graphic neural network reinforcement learning. Reinforcement learning adjusts the strategy from the actual system learning experience, and it is a process of gradually approaching the optimal strategy, mapping the image from high-dimensional gray space to low-dimensional feature category space; Then, the elastic registration of mutual information images is carried out in the feature class space. The advantage of this method is that it does not need extra supervision information, which effectively saves manpower and material resources. So as to make an accurate diagnosis or make a proper treatment plan.

Lung CT Image Registration through Landmark-constrained Learning with Convolutional Neural Network.

Accurate registration of lung computed tomography (CT) image is a significant task in thorax image analysis. Recently deep learning-based medical image registration methods develop fast and achieve promising performance on accuracy and speed. However, most of them learned the deformation field through intensity similarity but ignored the importance of aligning anatomical landmarks (e.g., the branch points of airway and vessels). Accurate alignment of anatomical landmarks is essential for obtaining anatomically correct registration. In this work, we propose landmark constrained learning with a convolutional neural network (CNN) for lung CT registration. Experimental results of 40 lung 3D CT images show that our method achieves 0.93 in terms of Dice index and 3.54 mm of landmark Euclidean distance on lung CT registration task, which outperforms state-of-the-art methods in registration accuracy.

ADMIR–Affine and Deformable Medical Image Registration for Drug-Addicted Brain Images

We proposed an unsupervised end-to-end Affine and Deformable Medical Image Registration (ADMIR) method based on convolutional neural network (ConvNet). ADMIR includes three key components: an affine registration module for learning the affine transformation parameters, a deformable registration module for learning the displacement vector field, and a spatial transformer for getting the final warped image from both affine and deformable transformation parameters. To evaluate its performance, the magnetic resonance images of drug-addicted brains were used to train and test the model, and we compared it with two state-of-art methods in terms of Dice score, Hausdorff distance, and average symmetric surface distance. The experimental results demonstrated that our proposed ADMIR model outperforms existing methods even with the images without pre-alignment, which suggests that the ADMIR model can be used to achieve quick medical image registration with high accuracy.