We propose a supervised nonrigid image registration method, trained using artificial displacement vector fields (DVF), for which we propose and compare three network architectures. The artificial DVFs allow training in a fully supervised and voxel-wise dense manner, but without the cost usually associated with the creation of densely labeled data. We propose a scheme to artificially generate DVFs, and for chest CT registration augment these with simulated respiratory motion. The proposed architectures are embedded in a multi-stage approach, to increase the capture range of the proposed networks in order to more accurately predict larger displacements. The proposed method, RegNet, is evaluated on multiple databases of chest CT scans and achieved a target registration error of 2.32 $\pm$ 5.33 mm and 1.86 $\pm$ 2.12 mm on SPREAD and DIR-Lab-4DCT studies, respectively. The average inference time of RegNet with two stages is about 2.2 s.
Images taken at different times or positions undergo transformations such as rotation, scaling, skewing, and more. The process of aligning different images which have undergone transformations can be done via registration. Registration is desirable when analyzing time-series data for tracking, averaging, or differential diagnoses of diseases. Efficient registration methods exist for rigid (including linear or affine) transformations; however, for non-rigid (also known as non-affine) transformations, current methods are computationally expensive and time-consuming. In this report, I will explor…
University of Bern（伯尔尼大学）；CentraleSupélec（中央理工-高等电力学院）；Université Paris-Saclay（巴黎-萨克雷大学）；Groupe Hospitalier Cochin-Hôtel Dieu（科钦-主宫医院集团）；Université Paris Descartes（巴黎笛卡尔大学）；TheraPanacea
Image registration and in particular deformable registration methods are pillars of medical imaging. Inspired by the recent advances in deep learning, we propose in this paper, a novel convolutional neural network architecture that couples linear and deformable registration within a unified architecture endowed with near real-time performance. Our framework is modular with respect to the global transformation component, as well as with respect to the similarity function while it guarantees smooth displacement fields. We evaluate the performance of our network on the challenging problem of MRI …
The Hong Kong University of Science and Technology（香港科技大学）
Recent deep learning-based methods have shown promising results and runtime advantages in deformable image registration. However, analyzing the effects of hyperparameters and searching for optimal regularization parameters prove to be too prohibitive in deep learning-based methods. This is because it involves training a substantial number of separate models with distinct hyperparameter values. In this paper, we propose a conditional image registration method and a new self-supervised learning paradigm for deep deformable image registration. By learning the conditional features that are correla…
Universidade Federal de Pernambuco（伯南布哥联邦大学）；Varian Medical Systems, Imaging Laboratory GmbH（瓦里安医疗系统影像实验室有限公司）
We presented a 2D/3D MV image registration method based on a Convolutional Neural Network. Most of the traditional image registration method intensity-based, which use optimization algorithms to maximize the similarity between to images. Although these methods can achieve good results for kilovoltage images, the same does not occur for megavoltage images due to the lower image quality. Also, these methods most of the times do not present a good capture range. To deal with this problem, we propose the use of Convolutional Neural Network. The experiments were performed using a dataset of 50 brai…
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Northwestern Polytechnical University（西北工业大学）；Korea University（高丽大学）
In this paper, we propose a deep learning approach for image registration by predicting deformation from image appearance. Since obtaining ground-truth deformation fields for training can be challenging, we design a fully convolutional network that is subject to dual-guidance: (1) Coarse guidance using deformation fields obtained by an existing registration method; and (2) Fine guidance using image similarity. The latter guidance helps avoid overly relying on the supervision from the training deformation fields, which could be inaccurate. For effective training, we further improve the deep con…
Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）
A novel non-rigid image registration algorithm is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of images to be registered in a self-supervised learning framework. Different from most existing deep learning based image registration methods that learn spatial transformations from training data with known corresponding spatial transformations, our method directly estimates spatial transformations between pairs of images by maximizing an image-wise similarity metric between fixed and deformed moving images, similar to conventional image…
The Hong Kong University of Science and Technology（香港科技大学）
Diffeomorphic deformable image registration is crucial in many medical image studies, as it offers unique, special properties including topology preservation and invertibility of the transformation. Recent deep learning-based deformable image registration methods achieve fast image registration by leveraging a convolutional neural network (CNN) to learn the spatial transformation from the synthetic ground truth or the similarity metric. However, these approaches often ignore the topology preservation of the transformation and the smoothness of the transformation which is enforced by a global s…
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心图像科学研究所）；Division of Image Processing, Leiden University Medical Center（莱顿大学医学中心图像处理部）
In this work we propose a deep learning network for deformable image registration (DIRNet). The DIRNet consists of a convolutional neural network (ConvNet) regressor, a spatial transformer, and a resampler. The ConvNet analyzes a pair of fixed and moving images and outputs parameters for the spatial transformer, which generates the displacement vector field that enables the resampler to warp the moving image to the fixed image. The DIRNet is trained end-to-end by unsupervised optimization of a similarity metric between input image pairs. A trained DIRNet can be applied to perform registration …
Johns Hopkins Medical Institutes（约翰霍普金斯医学研究所）；Johns Hopkins University（约翰霍普金斯大学）
Objectives: Computerized phantoms play an essential role in various applications of medical imaging research. Although the existing computerized phantoms can model anatomical variations through organ and phantom scaling, this does not provide a way to fully reproduce anatomical variations seen in humans. However, having a population of phantoms that models the variations in patient anatomy and, in nuclear medicine, uptake realization is essential for comprehensive validation and training. In this work, we present a novel image registration method for creating highly anatomically detailed anthr…
Fast Diffeomorphic Image Registration using Patch based Fully Convolutional Networks
School of Computer and Electrical Engineering, Hunan University of Arts and Science（湖南文理学院计算机与电气工程学院）；Furong College, Hunan University of Arts and Science（湖南文理学院芙蓉学院）；The University of Hong Kong（香港大学）
We propose a novel non-rigid image registration algorithm that is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of images to be registered. Different from most existing deep learning based image registration methods that learn spatial transformations from training data with known corresponding spatial transformations, our method directly estimates spatial transformations between pairs of images by maximizing an image-wise similarity metric between fixed and deformed moving images, similar to conventional image registration algorithms…
University of Pennsylvania（宾夕法尼亚大学）；Perelman School of Medicine（佩雷尔曼医学院）；Center for Biomedical Image Computing and Analytics (CBICA)（生物医学图像计算与分析中心（CBICA））
We present a diffeomorphic image registration algorithm to learn spatial transformations between pairs of images to be registered using fully convolutional networks (FCNs) under a self-supervised learning setting. The network is trained to estimate diffeomorphic spatial transformations between pairs of images by maximizing an image-wise similarity metric between fixed and warped moving images, similar to conventional image registration algorithms. It is implemented in a multi-resolution image registration framework to optimize and learn spatial transformations at different image resolutions jo…
3-D image registration, which involves aligning two or more images, is a critical step in a variety of medical applications from diagnosis to therapy. Image registration is commonly performed by optimizing an image matching metric as a cost function. However, this task is challenging due to the non-convex nature of the matching metric over the plausible registration parameter space and insufficient approaches for a robust optimization. As a result, current approaches are often customized to a specific problem and sensitive to image quality and artifacts. In this paper, we propose a completely …
Minutia-based palmprint recognition systems has got lots of interest in last two decades. Due to the large number of minutiae in a palmprint, approximately 1000 minutiae, the matching process is time consuming which makes it unpractical for real time applications. One way to address this issue is aligning all palmprint images to a reference image and bringing them to a same coordinate system. Bringing all palmprint images to a same coordinate system, results in fewer computations during minutia matching. In this paper, using convolutional neural network (CNN) and generalized Hough transform (G…
Johns Hopkins University（约翰斯·霍普金斯大学）；National Institute on Aging（国家老龄化研究所）；National Institute of Health（美国国立卫生研究院）
The majority of deep learning (DL) based deformable image registration methods use convolutional neural networks (CNNs) to estimate displacement fields from pairs of moving and fixed images. This, however, requires the convolutional kernels in the CNN to not only extract intensity features from the inputs but also understand image coordinate systems. We argue that the latter task is challenging for traditional CNNs, limiting their performance in registration tasks. To tackle this problem, we first introduce Coordinate Translator, a differentiable module that identifies matched features between…
University College London（伦敦大学学院）；University of Oxford（牛津大学）
One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from higher-level correspondence information contained in anatomical labels. We argue that such labels are more reliable and practical to obtain for reference sets of image pairs than voxel-level correspondence. Typical anatomical labels of interest may include solid organs, vessels, ducts, structure boundaries and other subject-specific ad hoc landmarks. The proposed end-t…
This paper presents a predictive model for estimating regularization parameters of diffeomorphic image registration. We introduce a novel framework that automatically determines the parameters controlling the smoothness of diffeomorphic transformations. Our method significantly reduces the effort of parameter tuning, which is time and labor-consuming. To achieve the goal, we develop a predictive model based on deep convolutional neural networks (CNN) that learns the mapping between pairwise images and the regularization parameter of image registration. In contrast to previous methods that esti…
University of Alberta（阿尔伯塔大学）；Servier Virtual Cardiac Centre（施维雅虚拟心脏中心）；Mazankowski Alberta Heart Institute（马赞科夫斯基阿尔伯塔心脏研究所）
Diffeomorphic deformable image registration is one of the crucial tasks in medical image analysis, which aims to find a unique transformation while preserving the topology and invertibility of the transformation. Deep convolutional neural networks (CNNs) have yielded well-suited approaches for image registration by learning the transformation priors from a large dataset. The improvement in the performance of these methods is related to their ability to learn information from several sample medical images that are difficult to obtain and bias the framework to the specific domain of data. In thi…
Westfälische Hochschule University of Applied Sciences（威斯特伐利亚应用科学大学）
Central venous catheters (CVC) are commonly inserted into the large veins of the neck, e.g. the internal jugular vein (IJV). CVC insertion may cause serious complications like misplacement into an artery or perforation of cervical vessels. Placing a CVC under sonographic guidance is an appropriate method to reduce such adverse events, if anatomical landmarks like venous and arterial vessels can be detected reliably. This task shall be solved by registration of patient individual images vs. an anatomically labelled reference image. In this work, a linear, affine transformation is performed on c…
University of Birmingham（伯明翰大学）；University of Melbourne（墨尔本大学）；University of Warwick（华威大学）；Northeast Forestry University（东北林业大学）；Alan Turing Institute（艾伦·图灵研究所）
Due to their extreme long-range modeling capability, vision transformer-based networks have become increasingly popular in deformable image registration. We believe, however, that the receptive field of a 5-layer convolutional U-Net is sufficient to capture accurate deformations without needing long-range dependencies. The purpose of this study is therefore to investigate whether U-Net-based methods are outdated compared to modern transformer-based approaches when applied to medical image registration. For this, we propose a large kernel U-Net (LKU-Net) by embedding a parallel convolutional bl…
The recent application of deep learning in various areas of medical image analysis has brought excellent performance gains. In particular, technologies based on deep learning in medical image registration can outperform traditional optimisation-based registration algorithms both in registration time and accuracy. However, the U-net based architectures used in most of the image registration frameworks downscale the data, which removes global information and affects the deformation. In this paper, we present a densely connected convolutional architecture for deformable image registration. Our pr…
University of Magdeburg（马格德堡大学）；University Hospital Magdeburg（马格德堡大学医院）
Recent studies demonstrated the eligibility of convolutional neural networks (CNNs) for solving the image registration problem. CNNs enable faster transformation estimation and greater generalization capability needed for better support during medical interventions. Conventional fully-supervised training requires a lot of high-quality ground truth data such as voxel-to-voxel transformations, which typically are attained in a too tedious and error-prone manner. In our work, we use weakly-supervised learning, which optimizes the model indirectly only via segmentation masks that are a more access…
Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins Medical Institutes（约翰斯·霍普金斯医学研究所）
In the last decade, convolutional neural networks (ConvNets) have dominated and achieved state-of-the-art performances in a variety of medical imaging applications. However, the performances of ConvNets are still limited by lacking the understanding of long-range spatial relations in an image. The recently proposed Vision Transformer (ViT) for image classification uses a purely self-attention-based model that learns long-range spatial relations to focus on the relevant parts of an image. Nevertheless, ViT emphasizes the low-resolution features because of the consecutive downsamplings, result i…
University of Oxford（牛津大学）；Institute of Biomedical Engineering（生物医学工程研究所）
Recent successes in deep learning based deformable image registration (DIR) methods have demonstrated that complex deformation can be learnt directly from data while reducing computation time when compared to traditional methods. However, the reliance on fully linear convolutional layers imposes a uniform sampling of pixel/voxel locations which ultimately limits their performance. To address this problem, we propose a novel approach of learning a continuous warp of the source image. Here, the required deformation vector fields are obtained from a concatenated linear and non-linear convolution …
Multi-modality image registration is one of the most underlined processes in medical image analysis. Recently, convolutional neural networks (CNNs) have shown significant potential in deformable registration. However, the lack of voxel-wise ground truth challenges the training of CNNs for an accurate registration. In this work, we propose a cross-modality similarity metric, based on the KL-divergence of image variables, and implement an efficient estimation method using a CNN. This estimation network, referred to as KLDivNet, can be trained unsupervisedly. We then embed the KLDivNet into a reg…
SINTEF（挪威科学与工业研究基金会）；Norwegian University of Science and Technology (NTNU)（挪威科技大学）；St. Olavs hospital, Trondheim University Hospital（圣奥拉夫医院，特隆赫姆大学医院）；Oslo University Hospital（奥斯陆大学医院）
Purpose: This study aims to explore training strategies to improve convolutional neural network-based image-to-image deformable registration for abdominal imaging. Methods: Different training strategies, loss functions, and transfer learning schemes were considered. Furthermore, an augmentation layer which generates artificial training image pairs on-the-fly was proposed, in addition to a loss layer that enables dynamic loss weighting. Results: Guiding registration using segmentations in the training step proved beneficial for deep-learning-based image registration. Finetuning the pretrained m…
Pattern Recognition Lab, FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡大学模式识别实验室）；Department of Ophthalmology, Universitätsklinikum Erlangen（埃尔朗根大学医院眼科）
Ophthalmological imaging utilizes different imaging systems, such as color fundus, infrared, fluorescein angiography, optical coherence tomography (OCT) or OCT angiography. Multiple images with different modalities or acquisition times are often analyzed for the diagnosis of retinal diseases. Automatically aligning the vessel structures in the images by means of multi-modal registration can support the ophthalmologists in their work. Our method uses a convolutional neural network to extract features of the vessel structure in multi-modal retinal images. We jointly train a keypoint detection an…
Tsinghua University（清华大学）；Shenzhen International Graduate School（深圳国际研究生院）；Harvard Medical School（哈佛医学院）；Brigham and Women’s Hospital（布莱根妇女医院）
The loss function of an unsupervised multimodal image registration framework has two terms, i.e., a metric for similarity measure and regularization. In the deep learning era, researchers proposed many approaches to automatically learn the similarity metric, which has been shown effective in improving registration performance. However, for the regularization term, most existing multimodal registration approaches still use a hand-crafted formula to impose artificial properties on the estimated deformation field. In this work, we propose a unimodal cyclic regularization training pipeline, which …
Southeast University（东南大学）；Tencent Healthcare Co.（腾讯医疗健康有限公司）；The Chinese University of Hong Kong（香港中文大学）
Recently, deep-learning-based approaches have been widely studied for deformable image registration task. However, most efforts directly map the composite image representation to spatial transformation through the convolutional neural network, ignoring its limited ability to capture spatial correspondence. On the other hand, Transformer can better characterize the spatial relationship with attention mechanism, its long-range dependency may be harmful to the registration task, where voxels with too large distances are unlikely to be corresponding pairs. In this study, we propose a novel Deforme…
Image Sciences Institute, University Medical Center Utrecht and Utrecht University（乌得勒支大学医学中心与乌得勒支大学影像科学研究所）；Division of Image Processing of the Leiden University Medical Center（莱顿大学医学中心图像处理部）
Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration. Thus far training of ConvNets for registration was supervised using predefined example registrations. However, obtaining example registrations is not trivial. To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Im…
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…
University of Utah（犹他大学）；University of Maryland, School of Medicine（马里兰大学医学院）
Radiation therapy presents a need for dynamic tracking of a target tumor volume. Fiducial markers such as implanted gold seeds have been used to gate radiation delivery but the markers are invasive and gating significantly increases treatment time. Pretreatment acquisition of a respiratory correlated 4DCT allows for determination of accurate motion tracking which is useful in treatment planning. We design a patient-specific motion subspace and a deep convolutional neural network to recover anatomical positions from a single fluoroscopic projection in real-time. We use this deep network to appr…
University of North Carolina, Chapel Hill（北卡罗来纳大学教堂山分校）
Deep convolutional neural networks (CNNs) are state-of-the-art for semantic image segmentation, but typically require many labeled training samples. Obtaining 3D segmentations of medical images for supervised training is difficult and labor intensive. Motivated by classical approaches for joint segmentation and registration we therefore propose a deep learning framework that jointly learns networks for image registration and image segmentation. In contrast to previous work on deep unsupervised image registration, which showed the benefit of weak supervision via image segmentations, our approac…
Georgia Institute of Technology（佐治亚理工学院）；Massachusetts Institute of Technology（麻省理工学院）；National Institute of Mental Health（美国国家精神卫生研究所）；The Chinese University of Hong Kong（香港中文大学）
Image registration has been widely studied over the past several decades, with numerous applications in science, engineering and medicine. Most of the conventional mathematical models for large deformation image registration rely on prescribed landmarks, which usually require tedious manual labeling and are prone to error. In recent years, there has been a surge of interest in the use of machine learning for image registration. In this paper, we develop a novel method for large deformation image registration by a fusion of quasiconformal theory and convolutional neural network (CNN). More spec…
Image registration is a crucial task in signal processing, but it often encounters issues with stability and efficiency. Non-learning registration approaches rely on optimizing similarity metrics between fixed and moving images, which can be expensive in terms of time and space complexity. This problem can be exacerbated when the images are large or there are significant deformations between them. Recently, deep learning, specifically convolutional neural network (CNN)-based methods, have been explored as an effective solution to the weaknesses of non-learning approaches. To further advance le…
CuraCloud corporation（库拉云公司）；Chengdu University of Information Technology（成都信息工程大学）
The crucial components of a conventional image registration method are the choice of the right feature representations and similarity measures. These two components, although elaborately designed, are somewhat handcrafted using human knowledge. To this end, these two components are tackled in an end-to-end manner via reinforcement learning in this work. Specifically, an artificial agent, which is composed of a combined policy and value network, is trained to adjust the moving image toward the right direction. We train this network using an asynchronous reinforcement learning algorithm, where a…
Medical image registration is a fundamental and critical task in medical image analysis. With the rapid development of deep learning, convolutional neural networks (CNN) have dominated the medical image registration field. Due to the disadvantage of the local receptive field of CNN, some recent registration methods have focused on using transformers for non-local registration. However, the standard Transformer has a vast number of parameters and high computational complexity, which causes Transformer can only be applied at the bottom of the registration models. As a result, only coarse informa…
Shanghai University（上海大学）；University of Copenhagen（哥本哈根大学）
In this paper, we propose a new approach to deformable image registration that captures sliding motions. The large deformation diffeomorphic metric mapping (LDDMM) registration method faces challenges in representing sliding motion since it per construction generates smooth warps. To address this issue, we extend LDDMM by incorporating both zeroth- and first-order momenta with a non-differentiable kernel. This allows to represent both discontinuous deformation at switching boundaries and diffeomorphic deformation in homogeneous regions. We provide a mathematical analysis of the proposed deform…
University College London（伦敦大学学院）；University of Oxford（牛津大学）
We describe an adversarial learning approach to constrain convolutional neural network training for image registration, replacing heuristic smoothness measures of displacement fields often used in these tasks. Using minimally-invasive prostate cancer intervention as an example application, we demonstrate the feasibility of utilizing biomechanical simulations to regularize a weakly-supervised anatomical-label-driven registration network for aligning pre-procedural magnetic resonance (MR) and 3D intra-procedural transrectal ultrasound (TRUS) images. A discriminator network is optimized to distin…
In ophthalmological imaging, multiple imaging systems, such as color fundus, infrared, fluorescein angiography, optical coherence tomography (OCT) or OCT angiography, are often involved to make a diagnosis of retinal disease. Multi-modal retinal registration techniques can assist ophthalmologists by providing a pixel-based comparison of aligned vessel structures in images from different modalities or acquisition times. To this end, we propose an end-to-end trainable deep learning method for multi-modal retinal image registration. Our method extracts convolutional features from the vessel struc…
Instituto Tecnológico y de Estudios Superiores de Monterrey（蒙特雷技术与高等教育学院）
Early tumor detection is key in reducing the number of breast cancer death and screening mammography is one of the most widely available and reliable method for early detection. However, it is difficult for the radiologist to process with the same attention each case, due the large amount of images to be read. Computer aided detection (CADe) systems improve tumor detection rate; but the current efficiency of these systems is not yet adequate and the correct interpretation of CADe outputs requires expert human intervention. Computer aided diagnosis systems (CADx) are being designed to improve c…
University College London（伦敦大学学院）；University of Oxford（牛津大学）
Spatially aligning medical images from different modalities remains a challenging task, especially for intraoperative applications that require fast and robust algorithms. We propose a weakly-supervised, label-driven formulation for learning 3D voxel correspondence from higher-level label correspondence, thereby bypassing classical intensity-based image similarity measures. During training, a convolutional neural network is optimised by outputting a dense displacement field (DDF) that warps a set of available anatomical labels from the moving image to match their corresponding counterparts in …
Domain-Shift Immunity in Deep Deformable Registration via Local Feature Representations
Kahlert School of Computing（卡尔伯特计算学校）；Scientific Computing and Imaging Institute（科学计算与成像研究所）；University of Utah（犹他大学）；Department of Biomedical Engineering（生物医学工程系）
Spatially Covariant Image Registration with Text Prompts
College of Electrical and Information Engineering, Hunan University（湖南大学电气与信息工程学院）；Cornell University（康奈尔大学）；University of Pennsylvania（宾夕法尼亚大学）；New York University（纽约大学）；Shanghai Jiao Tong University（上海交通大学）
Deformable image registration is able to achieve fast and accurate alignment between a pair of images and thus plays an important role in many medical image studies. The current deep learning (DL)-based image registration approaches directly learn the spatial transformation from one image to another by leveraging a convolutional neural network, requiring ground truth or similarity metric. Nevertheless, these methods only use a global similarity energy function to evaluate the similarity of a pair of images, which ignores the similarity of regions of interest (ROIs) within images. Moreover, DL-…
Rensselaer Polytechnic Institute（伦斯勒理工学院）；National Institutes of Health（美国国立卫生研究院）；National Cancer Institute（美国国家癌症研究所）
Prostate cancer biopsy benefits from accurate fusion of transrectal ultrasound (TRUS) and magnetic resonance (MR) images. In the past few years, convolutional neural networks (CNNs) have been proved powerful in extracting image features crucial for image registration. However, challenging applications and recent advances in computer vision suggest that CNNs are quite limited in its ability to understand spatial correspondence between features, a task in which the self-attention mechanism excels. This paper aims to develop a self-attention mechanism specifically for cross-modal image registrati…
University of California, Irvine（加州大学欧文分校）；Yale University（耶鲁大学）
Diffeomorphic image registration, offering smooth transformation and topology preservation, is required in many medical image analysis tasks.Traditional methods impose certain modeling constraints on the space of admissible transformations and use optimization to find the optimal transformation between two images. Specifying the right space of admissible transformations is challenging: the registration quality can be poor if the space is too restrictive, while the optimization can be hard to solve if the space is too general. Recent learning-based methods, utilizing deep neural networks to lea…
Rensselaer Polytechnic Institute（伦斯勒理工学院）；Philips Research North America（飞利浦北美研究院）；National Institutes of Health（美国国立卫生研究院）
Purpose: The fusion of transrectal ultrasound (TRUS) and magnetic resonance (MR) images for guiding targeted prostate biopsy has significantly improved the biopsy yield of aggressive cancers. A key component of MR-TRUS fusion is image registration. However, it is very challenging to obtain a robust automatic MR-TRUS registration due to the large appearance difference between the two imaging modalities. The work presented in this paper aims to tackle this problem by addressing two challenges: (i) the definition of a suitable similarity metric and (ii) the determination of a suitable optimizatio…
Graduate School of Informatics, Kyoto University（京都大学信息学研究科）；Graduate School of Medicine, Kyoto University（京都大学医学研究科）
Organ shape reconstruction based on a single-projection image during treatment has wide clinical scope, e.g., in image-guided radiotherapy and surgical guidance. We propose an image-to-graph convolutional network that achieves deformable registration of a 3D organ mesh for a single-viewpoint 2D projection image. This framework enables simultaneous training of two types of transformation: from the 2D projection image to a displacement map, and from the sampled per-vertex feature to a 3D displacement that satisfies the geometrical constraint of the mesh structure. Assuming application to radiati…
