Wing disc pouches of fruit flies are a powerful genetic model for studying physiological intercellular calcium ($Ca^{2+}$) signals for dynamic analysis of cell signaling in organ development and disease studies. A key to analyzing spatial-temporal patterns of $Ca^{2+}$ signal waves is to accurately align the pouches across image sequences. However, pouches in different image frames may exhibit extensive intensity oscillations due to $Ca^{2+}$ signaling dynamics, and commonly used multimodal non-rigid registration methods may fail to achieve satisfactory results. In this paper, we develop a new two-phase non-rigid registration approach to register pouches in image sequences. First, we conduct segmentation of the region of interest. (i.e., pouches) using a deep neural network model. Second, we obtain an optimal transformation and align pouches across the image sequences. Evaluated using both synthetic data and real pouch data, our method considerably outperforms the state-of-the-art non-rigid registration methods.
Multi-modal image registration is a challenging task that is vital to fuse complementary signals for subsequent analyses. Despite much research into cost functions addressing this challenge, there exist cases in which these are ineffective. In this work, we show that (1) this is true for the registration of in-vivo Drosophila brain volumes visualizing genetically encoded calcium indicators to an nc82 atlas and (2) that machine learning based contrast synthesis can yield improvements. More specifically, the number of subjects for which the registration outright failed was greatly reduced (from …
King’s College London（伦敦国王学院）；Siemens Healthineers（西门子医疗）
Registering CT images of the chest is a crucial step for several tasks such as disease progression tracking or surgical planning. It is also a challenging step because of the heterogeneous content of the human abdomen which implies complex deformations. In this work, we focus on accurately registering a subset of organs of interest. We register organ surface point clouds, as may typically be extracted from an automatic segmentation pipeline, by expanding the Bayesian Coherent Point Drift algorithm (BCPD). We introduce MO-BCPD, a multi-organ version of the BCPD algorithm which explicitly models…
Inria Nancy Grand Est（法国国家信息与自动化研究所南锡大东部分院）；Université de Lorraine（洛林大学）
An automatic elastic registration method suited for vascularized organs is proposed. The vasculature in both the preoperative and intra-operative images is represented as a graph. A typical application of this method is the fusion of pre-operative information onto the organ during surgery, to compensate for the limited details provided by the intra-operative imaging modality (e.g. CBCT) and to cope with changes in the shape of the organ. Due to image modalities differences and organ deformation, each graph has a different topology and shape. The Adaptive Compliance Graph Matching (ACGM) method…
A large calcium-imaging dataset reveals a systematic V4 organization for natural scenes
Peking University School of Life Sciences（北京大学生命科学学院）；IDG/McGovern Institute for Brain Research at Peking University（北京大学IDG/麦戈文脑科学研究所）；Key Laboratory of Machine Perception (Ministry of Education), Peking University（北京大学机器感知与智能教育部重点实验室）；Carnegie Mellon University（卡内基梅隆大学）
Human body is a complex dynamic system composed of various sub-dynamic parts. Especially, thoracic and abdominal organs have complex internal shape variations with different frequencies by various reasons such as respiration with fast motion and peristalsis with slower motion. CT protocols for abdominal lesions are multi-phase scans for various tumor detection to use different vascular contrast, however, they are not aligned well enough to visually check the same area. In this paper, we propose a time-efficient and accurate deformable registration algorithm for multi-phase CT scans considering…
University of Leeds（利兹大学）；Leeds Institute for Cardiovascular and Metabolic Medicine（利兹心血管与代谢医学研究所）；KU Leuven（荷语鲁汶大学）；Alan Turing Institute（艾伦·图灵研究所）
Medical image registration is a challenging task involving the estimation of spatial transformations to establish anatomical correspondence between pairs or groups of images. Recently, deep learning-based image registration methods have been widely explored, and demonstrated to enable fast and accurate image registration in a variety of applications. However, most deep learning-based registration methods assume that the deformation fields are smooth and continuous everywhere in the image domain, which is not always true, especially when registering images whose fields of view contain discontin…
Deformable registration of two-dimensional/three-dimensional (2D/3D) images of abdominal organs is a complicated task because the abdominal organs deform significantly and their contours are not detected in two-dimensional X-ray images. We propose a supervised deep learning framework that achieves 2D/3D deformable image registration between 3D volumes and single-viewpoint 2D projected images. The proposed method learns the translation from the target 2D projection images and the initial 3D volume to 3D displacement fields. In experiments, we registered 3D-computed tomography (CT) volumes to di…
ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究中心）
Image registration of liver dynamic contrast-enhanced computed tomography (DCE-CT) is crucial for diagnosis and image-guided surgical planning of liver cancer. However, intensity variations due to the flow of contrast agents combined with complex spatial motion induced by respiration brings great challenge to existing intensity-based registration methods. To address these problems, we propose a novel structure-aware registration method by incorporating structural information of related organs with segmentation-guided deep registration network. Existing segmentation-guided registration methods …
We propose a registration algorithm for 2D CT/MRI medical images with a new unsupervised end-to-end strategy using convolutional neural networks. The contributions of our algorithm are threefold: (1) We transplant traditional image registration algorithms to an end-to-end convolutional neural network framework, while maintaining the unsupervised nature of image registration problems. The image-to-image integrated framework can simultaneously learn both image features and transformation matrix for registration. (2) Training with additional data without any label can further improve the registra…
AGH University of Science and Technology（AGH科学技术大学）
In this paper, we present a short description of the method proposed to ANHIR challenge organized jointly with the IEEE ISBI 2019 conference. We propose a method consisting of preprocessing, initial alignment, nonrigid registration algorithms and a method to automatically choose the best result. The method turned out to be robust (99.792% robustness) and accurate (0.38% average median rTRE). The main drawback of the proposed method is relatively high computation time. However, this aspect can be easily improved by cleaning the code and proposing a GPU implementation.
Recent progress in tissue clearing has allowed for the imaging of entire organs at single-cell resolution. These methods produce very large 3D images (several gigabytes for a whole mouse brain). A necessary step in analysing these images is registration across samples. Existing methods of registration were developed for lower resolution image modalities (e.g. MRI) and it is unclear whether their performance and accuracy is satisfactory at this larger scale. In this study, we used data from different mouse brains cleared with the CUBIC protocol to evaluate five freely available image registrati…
DGIST（大邱庆北科学技术院）；Yeungnam University College of Medicine（岭南大学医学院）；Yeungnam University Hospital（岭南大学医院）
Non-rigid registration is a necessary but challenging task in medical imaging studies. Recently, unsupervised registration models have shown good performance, but they often require a large-scale training dataset and long training times. Therefore, in real world application where only dozens to hundreds of image pairs are available, existing models cannot be practically used. To address these limitations, we propose a novel unsupervised registration model which is integrated with a gradient-based meta learning framework. In particular, we train a meta learner which finds an initialization poin…
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…
Automatic Registration of SHG and H&E Images with Feature-based Initial Alignment and Intensity-based Instance Optimization: Contribution to the COMULIS Challenge
AGH University of Krakow（克拉科夫AGH科技大学）；University of Applied Sciences Western Switzerland (HES-SO Valais-Wallis)（瑞士西部应用科学大学（瓦莱州-瓦利斯））；University of Geneva（日内瓦大学）
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（布莱根和妇女医院）
Multimodal image registration (MIR) is a fundamental procedure in many image-guided therapies. Recently, unsupervised learning-based methods have demonstrated promising performance over accuracy and efficiency in deformable image registration. However, the estimated deformation fields of the existing methods fully rely on the to-be-registered image pair. It is difficult for the networks to be aware of the mismatched boundaries, resulting in unsatisfactory organ boundary alignment. In this paper, we propose a novel multimodal registration framework, which leverages the deformation fields estima…
A Multimodal Clinically Informed Coarse-to-Fine Framework for Longitudinal CT Registration in Proton Therapy
Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA（梅奥诊所放射肿瘤科）；School of Biomedical Engineering, ShanghaiTech University, Shanghai, China（上海科技大学生物医学工程学院）；Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200230, China（上海联合影像智能科技有限公司）；Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China（上海人工智能实验室）；Shanghai Clinical Research and Trial Center, Shanghai 201210, China（上海临床研究与试验中心）
Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) automatic 3-D registration is implemented and validated for small animal image volumes so that the high-resolution anatomical MRI information can be fused with the low spatial resolution of functional PET information for the localization of lesion that is currently in high demand in the study of tumor of cancer (oncology) and its corresponding preparation of pharmaceutical drugs. Though many registration algorithms are developed and applied on human brain volumes, these methods may not be as efficient on small animal datas…
University of Oxford（牛津大学）；University College London（伦敦大学学院）；InstaDeep（InstaDeep公司）；City University of Hong Kong（香港城市大学）；Radboud University Nijmegen Medical Centre（拉德堡德大学奈梅亨医学中心）
The ability to adapt medical image segmentation networks for a novel class such as an unseen anatomical or pathological structure, when only a few labelled examples of this class are available from local healthcare providers, is sought-after. This potentially addresses two widely recognised limitations in deploying modern deep learning models to clinical practice, expertise-and-labour-intensive labelling and cross-institution generalisation. This work presents the first 3D few-shot interclass segmentation network for medical images, using a labelled multi-institution dataset from prostate canc…
Recursive Deformable Image Registration Network with Mutual Attention
The Kennedy Institute of Rheumatology（肯尼迪风湿病学研究所）；University of Oxford（牛津大学）；Big Data Institute（大数据研究所）；Imperial College London（伦敦帝国学院）；Nuffield Department of Population Health, University of Oxford（牛津大学纳菲尔德人口健康系）
SAME++: A Self-supervised Anatomical eMbeddings Enhanced medical image registration framework using stable sampling and regularized transformation
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Alibaba Group（阿里巴巴集团）；Johns Hopkins University（约翰斯·霍普金斯大学）；The First Affiliated Hospital of Zhejiang University（浙江大学附属第一医院）
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）
Method: ProRSeg was trained using 5-fold cross-validation with 110 T2-weighted MRI acquired at 5 treatment fractions from 10 different patients, taking care that same patient scans were not placed in training and testing folds. Segmentation accuracy was measured using Dice similarity coefficient (DSC) and Hausdorff distance at 95th percentile (HD95). Registration consistency was measured using coefficient of variation (CV) in displacement of OARs. Ablation tests and accuracy comparisons against multiple methods were done. Finally, applicability of ProRSeg to segment cone-beam CT (CBCT) scans w…
Purpose: Segmentation of organs-at-risk (OARs) is a bottleneck in current radiation oncology pipelines and is often time consuming and labor intensive. In this paper, we propose an atlas-based semi-supervised registration algorithm to generate accurate segmentations of OARs for which there are ground truth contours and rough segmentations of all other OARs in the atlas. To the best of our knowledge, this is the first study to use learning-based registration methods for the segmentation of head and neck patients and demonstrate its utility in clinical applications. Methods: Our algorithm cascad…
Deformable Image Registration (DIR) of MR and CT images is one of the most challenging registration task, due to the inherent structural differences of the modalities and the missing dense ground truth. Recently cycle Generative Adversarial Networks (cycle-GANs) have been used to learn the intensity relationship between these 2 modalities for unpaired brain data. Yet its usefulness for DIR was not assessed. In this study we evaluate the DIR performance for thoracic and abdominal organs after synthesis by cycle-GAN. We show that geometric changes, which differentiate the two populations (e.g. i…
Xidian University（西安电子科技大学）；Robarts Research Institute（罗巴茨研究所）；Western University（韦仕敦大学）
Recently, diagnosis, therapy and monitoring of human diseases involve a variety of imaging modalities, such as magnetic resonance imaging(MRI), computed tomography(CT), Ultrasound(US) and Positron-emission tomography(PET) as well as a variety of modern optical techniques. Over the past two decade, it has been recognized that advanced image processing techniques provide valuable information to physicians for diagnosis, image guided therapy and surgery, and monitoring of the treated organ to the therapy. Many researchers and companies have invested significant efforts in the developments of adva…
Tsinghua University（清华大学）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）
Deformable image registration (DIR) is essential for many image-guided therapies. Recently, deep learning approaches have gained substantial popularity and success in DIR. Most deep learning approaches use the so-called mono-stream "high-to-low, low-to-high" network structure, and can achieve satisfactory overall registration results. However, accurate alignments for some severely deformed local regions, which are crucial for pinpointing surgical targets, are often overlooked. Consequently, these approaches are not sensitive to some hard-to-align regions, e.g., intra-patient registration of de…
TotalRegistrator: Towards a Lightweight Foundation Model for CT Image Registration
Department of Imaging, Radboudumc, Nijmegen, the Netherlands（影像部，Radboudumc，尼姆egen，荷兰）；Institute for Medical Informatics, University of Lübeck, Lübeck, Germany（医学信息研究所，吕贝克大学，吕贝克，德国）；Department of Nuclear Medicine, Hospital 108, Hanoi, Vietnam（核医学部，108医院，河内，越南）；Department of Diagnostic Imaging and Interventional Radiology, Thai Nguyen National Hospital, Thai Nguyen, Vietnam（诊断影像和介入放射科，Thai Nguyen国家医院，Thai Nguyen，越南）；Interventional Radiology Center, Tam Anh Hospital, Hanoi, Vietnam（介入放射科中心，Tam Anh医院，河内，越南）；FET, Vietnam National University, University of Engineering and Technology, Hanoi, Vietnam（FET，越南国家大学，工程与技术大学，河内，越南）
Real-time 3D navigation during minimally invasive procedures is an essential yet challenging task, especially when considerable tissue motion is involved. To balance image acquisition speed and resolution, only 2D images or low-resolution 3D volumes can be used clinically. In this paper, a real-time and registration-free framework for dynamic shape instantiation, generalizable to multiple anatomical applications, is proposed to instantiate high-resolution 3D shapes of an organ from a single 2D image intra-operatively. Firstly, an approximate optimal scan plane was determined by analyzing the p…
Mamba? Catch The Hype Or Rethink What Really Helps for Image Registration
Technical University of Munich（慕尼黑工业大学）；Klinikum Rechts der Isar（慕尼黑右岸医院）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Imperial College London（伦敦帝国学院）
College of Physics and Information Engineering, Fuzhou University（福州大学物理与信息工程学院）；School of Data Science, Fudan University（复旦大学大数据学院）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）
Multi-modality medical images can provide relevant or complementary information for a target (organ, tumor or tissue). Registering multi-modality images to a common space can fuse these comprehensive information, and bring convenience for clinical application. Recently, neural networks have been widely investigated to boost registration methods. However, it is still challenging to develop a multi-modality registration network due to the lack of robust criteria for network training. In this work, we propose a multi-modality registration network (MMRegNet), which can perform registration between…
Johns Hopkins University（约翰斯·霍普金斯大学）；PAII Inc.（PAII公司）；Ping An Technology（平安科技）；The First Affiliated Hospital Zhejiang University（浙江大学医学院附属第一医院）
In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration. This work is built on top of a recent algorithm SAM, which is capable of computing dense anatomical/semantic correspondences between two images at the pixel level. Our method is named SAME, which breaks down image registration into three steps: affine transformation, coarse deformation, and deep deformable registration. Using SAM embeddings, we enhance these steps by finding more coherent correspondences, and providing features and a loss function with better semantic guidance. We collect a mu…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；Temple University（天普大学）
There has been a long pursuit for precise and reproducible glomerular quantification on renal pathology to leverage both research and practice. When digitizing the biopsy tissue samples using whole slide imaging (WSI), a set of serial sections from the same tissue can be acquired as a stack of images, similar to frames in a video. In radiology, the stack of images (e.g., computed tomography) are naturally used to provide 3D context for organs, tissues, and tumors. In pathology, it is appealing to do a similar 3D assessment. However, the 3D identification and association of large-scale glomerul…
STAR: A Fast and Robust Rigid Registration Framework for Serial Histopathological Images
School of Biological Science and Medical Engineering, Beihang University, Beijing, China（生物科学与医学工程学院，北航，北京，中国）；School of Computer Science and Engineering, Beihang University, Beijing, China（计算机科学与工程学院，北航，北京，中国）
The establishment of image correspondence through robust image registration is critical to many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring, and is a very challenging problem. Since the beginning of the recent deep learning renaissance, the medical imaging research community has developed deep learning based approaches and achieved the state-of-the-art in many applications, including image registration. The rapid adoption of deep learning for image registration applications over the past few years necessitates a comprehensive summary and outlook, whic…
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…
University College London（伦敦大学学院）；University of Leeds（利兹大学）
Biomechanical modelling of soft tissue provides a non-data-driven method for constraining medical image registration, such that the estimated spatial transformation is considered biophysically plausible. This has not only been adopted in real-world clinical applications, such as the MR-to-ultrasound registration for prostate intervention of interest in this work, but also provides an explainable means of understanding the organ motion and spatial correspondence establishment. This work instantiates the recently-proposed physics-informed neural networks (PINNs) to a 3D linear elastic model for …
Unsupervised Method for Intra-patient Registration of Brain Magnetic Resonance Images based on Objective Function Weighting by Inverse Consistency: Contribution to the BraTS-Reg Challenge
University of Applied Sciences Western Switzerland（瑞士西部应用科学大学）；AGH University of Science and Technology（AGH科技大学）；University of Geneva（日内瓦大学）；University of Padova（帕多瓦大学）
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Kitware, Inc.（Kitware公司）
Registration is widely used in image-guided therapy and image-guided surgery to estimate spatial correspondences between organs of interest between planning and treatment images. However, while high-quality computed tomography (CT) images are often available at planning time, limited angle acquisitions are frequently used during treatment because of radiation concerns or imaging time constraints. This requires algorithms to register CT images based on limited angle acquisitions. We, therefore, formulate a 3D/2D registration approach which infers a 3D deformation based on measured projections a…
Automatic liver segmentation plays an important role in computer-aided diagnosis and treatment. Manual segmentation of organs is a difficult and tedious task and so prone to human errors. In this paper, we propose an adaptive 3D region growing with subject-specific conditions. For this aim we use the intensity distribution of most probable voxels in prior map along with location prior. We also incorporate the boundary of target organs to restrict the region growing. In order to obtain strong edges and high contrast, we propose an effective contrast enhancement algorithm to facilitate more accu…
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）
Image-guided adaptive lung radiotherapy requires accurate tumor and organs segmentation from during treatment cone-beam CT (CBCT) images. Thoracic CBCTs are hard to segment because of low soft-tissue contrast, imaging artifacts, respiratory motion, and large treatment induced intra-thoracic anatomic changes. Hence, we developed a novel Patient-specific Anatomic Context and Shape prior or PACS-aware 3D recurrent registration-segmentation network for longitudinal thoracic CBCT segmentation. Segmentation and registration networks were concurrently trained in an end-to-end framework and implemente…
Computational anatomy allows the quantitative analysis of organs in medical images. However, most analysis is constrained to the millimeter scale because of the limited resolution of clinical computed tomography (CT). X-ray microtomography ($μ$CT) on the other hand allows imaging of ex-vivo tissues at a resolution of tens of microns. In this work, we use clinical CT to image lung cancer patients before partial pneumonectomy (resection of pathological lung tissue). The resected specimen is prepared for $μ$CT imaging at a voxel resolution of 50 $μ$m (0.05 mm). This high-resolution image of the l…
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…
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 …
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）
LoRCA: LoRA Cycle Adaptation for Histology to HiP-CT Translation with DINOv3
University College London（伦敦大学学院）；University of Cambridge（剑桥大学）；Cambridge Institute of Therapeutic Immunology and Infectious Diseases（剑桥治疗免疫学与传染病研究所）；Wellcome Trust Sanger Institute（惠康基金会桑格研究所）；Cambridge Stem Cell Institute（剑桥干细胞研究所）；Centre for AI in Medicine（医学人工智能中心）；Cavendish Laboratory（卡文迪什实验室）；Medical University of Vienna（维也纳医科大学）；European Synchrotron Radiation Facility（欧洲同步辐射装置）；CIFAR（加拿大高级研究所）；Cambridge University Hospitals NHS Foundation Trust（剑桥大学医院NHS基金会信托基金）；NIHR Cambridge Biomedical Research Centre（英国国家健康与研究院剑桥生物医学研究中心）
A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；School of Mathematics, Sun Yat-sen University（中山大学数学学院）；Stanford University（斯坦福大学）；Guangdong Medical University（广东医科大学）；Xiaohe Healthcare ByteDance（字节跳动小河医疗）
