In this research paper, authors propose multimodal brain image registration using discrete wavelet transform(DWT) followed by Gaussian pyramids. The reference and target images are decomposed into their LL, LH, HL and LL DWT coefficients and then are processed for image registration using Gaussian pyramids. The image registration is also done using Gaussian pyramids only and wavelets transforms only for comparison. The quality of registration is measured by comparing the maximum MI values used by the three methods and also by comparing their correlation coefficients. Our proposed technique proves to show better results when compared with the other two methods.
The Hong Kong University of Science and Technology（香港科技大学）
Deep learning-based methods have recently demonstrated promising results in deformable image registration for a wide range of medical image analysis tasks. However, existing deep learning-based methods are usually limited to small deformation settings, and desirable properties of the transformation including bijective mapping and topology preservation are often being ignored by these approaches. In this paper, we propose a deep Laplacian Pyramid Image Registration Network, which can solve the image registration optimization problem in a coarse-to-fine fashion within the space of diffeomorphic …
Cluster of Excellence – ML for Science（卓越集群——科学机器学习）；University of Tübingen（蒂宾根大学）；Faculty of Health Sciences and Medicine, University of Lucerne（卢塞恩大学健康科学与医学院）；University of Lübeck（吕贝克大学）
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…
RVS College of Engineering and Technology（RVS工程技术学院）
In recent years, many research achievements are made in the medical image fusion field. Fusion is basically extraction of best of inputs and conveying it to the output. Medical Image fusion means that several of various modality image information is comprehended together to form one image to express its information. The aim of image fusion is to integrate complementary and redundant information. In this paper, a multimodal image fusion algorithm based on the dual-tree complex wavelet transform (DT-CWT) and adaptive particle swarm optimization (APSO) is proposed. Fusion is achieved through the …
The Hong Kong University of Science and Technology（香港科技大学）
Registration of pre-operative and follow-up brain MRI scans is challenging due to the large variation of tissue appearance and missing correspondences in tumour recurrence regions caused by tumour mass effect. Although recent deep learning-based deformable registration methods have achieved remarkable success in various medical applications, most of them are not capable of registering images with pathologies. In this paper, we propose a 3-step registration pipeline for pre-operative and follow-up brain MRI scans that consists of 1) a multi-level affine registration, 2) a conditional deep Lapla…
Northern Arizona University（北亚利桑那大学）；Xi’an Network Computing Data Technology Co., Ltd.（西安网络计算数据技术有限公司）
Convolutional layers in Artificial Neural Networks (ANN) treat the channel features equally without feature selection flexibility. While using ANNs for image denoising in real-world applications with unknown noise distributions, particularly structured noise with learnable patterns, modeling informative features can substantially boost the performance. Channel attention methods in real image denoising tasks exploit dependencies between the feature channels, hence being a frequency component filtering mechanism. Existing channel attention modules typically use global statics as descriptors to l…
A Symmetric Dynamic Learning Framework for Diffeomorphic Medical Image Registration
Xiangtan University（湘潭大学）；University of Strathclyde（思克莱德大学）；Nankai University（南开大学）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Manchester（曼彻斯特大学）
University of Warwick（华威大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）；The Alan Turing Institute（艾伦·图灵研究所）；University Hospitals Coventry & Warwickshire（考文垂与沃里克郡大学医院）
Cross-slide image analysis provides additional information by analysing the expression of different biomarkers as compared to a single slide analysis. These biomarker stained slides are analysed side by side, revealing unknown relations between them. During the slide preparation, a tissue section may be placed at an arbitrary orientation as compared to other sections of the same tissue block. The problem is compounded by the fact that tissue contents are likely to change from one section to the next and there may be unique artefacts on some of the slides. This makes registration of each sectio…
Ecole Centrale de Nantes（南特中央理工学院）；Keosys Medical Imaging（Keosys医学影像公司）；University of Nantes（南特大学）；University of Angers（昂热大学）；ICO Cancer Center（ICO癌症中心）
Longitudinal image registration is challenging and has not yet benefited from major performance improvements thanks to deep-learning. Inspired by Deep Image Prior, this paper introduces a different use of deep architectures as regularizers to tackle the image registration question. We propose a subject-specific deformable registration method called MIRRBA, relying on a deep pyramidal architecture to be the prior parametric model constraining the deformation field. Diverging from the supervised learning paradigm, MIRRBA does not require a learning database, but only the pair of images to be reg…
Disentangling Progress in Medical Image Registration: Beyond Trend-Driven Architectures towards Domain-Specific Strategies
Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Imperial College London（伦敦帝国理工学院）；University of Pennsylvania（宾夕法尼亚大学）；National University of Singapore（新加坡国立大学）
RegWSI: Whole Slide Image Registration using Combined Deep Feature- and Intensity-Based Methods: Winner of the ACROBAT 2023 Challenge
University of Applied Sciences Western Switzerland（瑞士西应用科学大学）；AGH University of Kraków（克拉科夫AGH大学）；University of Padova（帕多瓦大学）；University of Geneva（日内瓦大学）
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…
Wavelet-Assisted Multi-Frequency Attention Network for Pansharpening
University of Electronic Science and Technology of China（电子科技大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges
EchoScout GmbH；Institute of Medical Informatics（医学信息学研究所）；Institute of Applied Medical Informatics（应用医学信息学研究所）；Institute of Computational Neuroscience（计算神经科学研究所）；School of Artificial Intelligence and Robotics（人工智能与机器人学院）；Cornell University（康奈尔大学）；University of Electronic Science and Technology of China（电子科技大学）；Mechanical Engineering Department（机械工程系）；Harvard Medical School（哈佛医学院）；Technical University of Munich（慕尼黑技术大学）；Aalto University（阿德莱德大学）；Canon Medical Systems (China) Co., Ltd.（佳能医疗系统（中国）有限公司）；Smart Medical Imaging, Learning and Engineering (SMLE) Lab（智能医学影像、学习和工程实验室）；School of Information Science and Engineering（信息科学与工程学院）；Department of Imaging Physics（影像物理系）；Clinical Computational Medical Imaging Research（临床计算医学成像研究）；TUM Klinikum Rechts der Isar（慕尼黑工业大学医院）；University of California（加州大学）；AGH University of Krakow（克拉科夫应用科学大学）
