Neural Multi-Atlas Label Fusion: Application to Cardiac MR Images
Xi’an Jiaotong University（西安交通大学）；National Engineering Laboratory for Big Data Algorithm and Analysis Technology（大数据算法与分析技术国家工程实验室）；Shanghai Jiaotong University（上海交通大学）
Multi-atlas segmentation approach is one of the most widely-used image segmentation techniques in biomedical applications. There are two major challenges in this category of methods, i.e., atlas selection and label fusion. In this paper, we propose a novel multi-atlas segmentation method that formulates multi-atlas segmentation in a deep learning framework for better solving these challenges. The proposed method, dubbed deep fusion net (DFN), is a deep architecture that integrates a feature extraction subnet and a non-local patch-based label fusion (NL-PLF) subnet in a single network. The network parameters are learned by end-to-end training for automatically learning deep features that enable optimal performance in a NL-PLF framework. The learned deep features are further utilized in defining a similarity measure for atlas selection. By evaluating on two public cardiac MR datasets of SATA-13 and LV-09 for left ventricle segmentation, our approach achieved 0.833 in averaged Dice metric (ADM) on SATA-13 dataset and 0.95 in ADM for epicardium segmentation on LV-09 dataset, comparing favorably with the other automatic left ventricle segmentation methods. We also tested our approach on Cardiac Atlas Project (CAP) testing set of MICCAI 2013 SATA Segmentation Challenge, and our method achieved 0.815 in ADM, ranking highest at the time of writing.
Vanderbilt University（范德堡大学）；National Institute on Aging（美国国家老龄化研究所）
Longitudinal reproducibility is an essential concern in automated medical image segmentation, yet has proven to be an elusive objective as manual brain structure tracings have shown more than 10% variability. To improve reproducibility, lon-gitudinal segmentation (4D) approaches have been investigated to reconcile tem-poral variations with traditional 3D approaches. In the past decade, multi-atlas la-bel fusion has become a state-of-the-art segmentation technique for 3D image and many efforts have been made to adapt it to a 4D longitudinal fashion. However, the previous methods were either lim…
The Hong Kong University of Science and Technology（香港科技大学）
In this paper, a novel label fusion method is proposed for brain magnetic resonance image segmentation. This label fusion method is formulated on a graph, which embraces both label priors from atlases and anatomical priors from target image. To represent a pixel in a comprehensive way, three kinds of feature vectors are generated, including intensity, gradient and structural signature. To select candidate atlas nodes for fusion, rather than exact searching, randomized k-d tree with spatial constraint is introduced as an efficient approximation for high-dimensional feature matching. Feature Sen…
In this work, we attempt the segmentation of cardiac structures in late gadolinium-enhanced (LGE) magnetic resonance images (MRI) using only minimal supervision in a two-step approach. In the first step, we register a small set of five LGE cardiac magnetic resonance (CMR) images with ground truth labels to a set of 40 target LGE CMR images without annotation. Each manually annotated ground truth provides labels of the myocardium and the left ventricle (LV) and right ventricle (RV) cavities, which are used as atlases. After multi-atlas label fusion by majority voting, we possess noisy labels fo…
Imperial College London（帝国理工学院）；MRC London Institute of Medical Sciences（MRC伦敦医学科学研究所）；National Heart and Lung Institute（国家心肺研究所）
Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-constrained bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisat…
Magnetic Resonance Imaging (MRI) has evolved as a clinical standard-of-care imaging modality for cardiac morphology, function assessment, and guidance of cardiac interventions. All these applications rely on accurate extraction of the myocardial tissue and blood pool from the imaging data. Here we propose a framework for left ventricle (LV) segmentation from cardiac cine-MRI. First, we segment the LV blood pool using iterative graph cuts, and subsequently use this information to segment the myocardium. We formulate the segmentation procedure as an energy minimization problem in a graph subject…
Wuhan University（武汉大学）；Rensselaer Polytechnic Institute (RPI)（伦斯勒理工学院（RPI））
Medical image segmentation is a fundamental task in medical image analysis. Despite that deep convolutional neural networks have gained stellar performance in this challenging task, they typically rely on large labeled datasets, which have limited their extension to customized applications. By revisiting the superiority of atlas based segmentation methods, we present a new framework of One-pass aligned Atlas Set for Images Segmentation (OASIS). To address the problem of time-consuming iterative image registration used for atlas warping, the proposed method takes advantage of the power of deep …
Due to the imbalanced and limited data, semi-supervised medical image segmentation methods often fail to produce superior performance for some specific tailed classes. Inadequate training for those particular classes could introduce more noise to the generated pseudo labels, affecting overall learning. To alleviate this shortcoming and identify the under-performing classes, we propose maintaining a confidence array that records class-wise performance during training. A fuzzy fusion of these confidence scores is proposed to adaptively prioritize individual confidence metrics in every sample rat…
King's College London（伦敦国王学院）；St Thomas' Hospital NHS Foundation Trust（圣托马斯医院NHS基金会信托）；Philips Research（飞利浦研究院）；Sanofi R&D（赛诺菲研发中心）
The availability of large scale databases containing imaging and non-imaging data, such as the UK Biobank, represents an opportunity to improve our understanding of healthy and diseased bodily function. Cardiac motion atlases provide a space of reference in which the motion fields of a cohort of subjects can be directly compared. In this work, a cardiac motion atlas is built from cine MR data from the UK Biobank (~ 6000 subjects). Two automated quality control strategies are proposed to reject subjects with insufficient image quality. Based on the atlas, three dimensionality reduction algorith…
Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；School of Computer and Control Engineering, Yantai University（烟台大学计算机与控制工程学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
A novel multi-atlas based image segmentation method is proposed by integrating a semi-supervised label propagation method and a supervised random forests method in a pattern recognition based label fusion framework. The semi-supervised label propagation method takes into consideration local and global image appearance of images to be segmented and segments the images by propagating reliable segmentation results obtained by the supervised random forests method. Particularly, the random forests method is used to train a regression model based on image patches of atlas images for each voxel of th…
University of Central Florida（中佛罗里达大学）；Florida Hospital（佛罗里达医院）
Anatomical and biophysical modeling of left atrium (LA) and proximal pulmonary veins (PPVs) is important for clinical management of several cardiac diseases. Magnetic resonance imaging (MRI) allows qualitative assessment of LA and PPVs through visualization. However, there is a strong need for an advanced image segmentation method to be applied to cardiac MRI for quantitative analysis of LA and PPVs. In this study, we address this unmet clinical need by exploring a new deep learning-based segmentation strategy for quantification of LA and PPVs with high accuracy and heightened efficiency. Our …
Multi-atlas segmentation (MAS) is a popular image segmentation technique for medical images. In this work, we improve the performance of MAS by correcting registration errors before label fusion. Specifically, we use a volumetric displacement field to refine registrations based on image anatomical appearance and predicted labels. We show the influence of the initial spatial alignment as well as the beneficial effect of using label information for MAS performance. Experiments demonstrate that the proposed refinement approach improves MAS performance on a 3D magnetic resonance dataset of the kne…
Harbin Institute of Technology（哈尔滨工业大学）；University of Manchester（曼彻斯特大学）
Left ventricular (LV) volumes estimation is a critical procedure for cardiac disease diagnosis. The objective of this paper is to address direct LV volumes prediction task. Methods: In this paper, we propose a direct volumes prediction method based on the end-to-end deep convolutional neural networks (CNN). We study the end-to-end LV volumes prediction method in items of the data preprocessing, networks structure, and multi-views fusion strategy. The main contributions of this paper are the following aspects. First, we propose a new data preprocessing method on cardiac magnetic resonance (CMR)…
Accurate and robust whole heart substructure segmentation is crucial in developing clinical applications, such as computer-aided diagnosis and computer-aided surgery. However, segmentation of different heart substructures is challenging because of inadequate edge or boundary information, the complexity of the background and texture, and the diversity in different substructures' sizes and shapes. This article proposes a framework for multi-class whole heart segmentation employing non-rigid registration-based probabilistic atlas incorporating the Bayesian framework. We also propose a non-rigid r…
University College London（伦敦大学学院）；Centre for Medical Image Computing（医学图像计算中心）
Accurate segmentation of the right ventricle (RV) is a crucial step in the assessment of the ventricular structure and function. Yet, due to its complex anatomy and motion segmentation of the RV has not been as largely studied as the left ventricle. This paper presents a fully automatic method for the segmentation of the RV in cardiac magnetic resonance images (MRI). The method uses a coarse-to-fine segmentation strategy in combination with a multi-atlas propagation segmentation framework. Based on a cross correlation metric, our method selects the best atlases for propagation allowing the ref…
In this work, we improve the performance of multi-atlas segmentation (MAS) by integrating the recently proposed VoteNet model with the joint label fusion (JLF) approach. Specifically, we first illustrate that using a deep convolutional neural network to predict atlas probabilities can better distinguish correct atlas labels from incorrect ones than relying on image intensity difference as is typical in JLF. Motivated by this finding, we propose VoteNet+, an improved deep network to locally predict the probability of an atlas label to differs from the label of the target image. Furthermore, we …
AVP-AP: Self-supervised Automatic View Positioning in 3D cardiac CT via Atlas Prompting
Beihang University（北京航空航天大学）；School of Instrumentation and Optoelectronic Engineering, Beihang University（北京航空航天大学仪器科学与光电工程学院）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；School of Biological Sciences, Victoria University of Wellington（惠灵顿维多利亚大学生物科学学院）；Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）
Deep learning (DL) approaches are state-of-the-art for many medical image segmentation tasks. They offer a number of advantages: they can be trained for specific tasks, computations are fast at test time, and segmentation quality is typically high. In contrast, previously popular multi-atlas segmentation (MAS) methods are relatively slow (as they rely on costly registrations) and even though sophisticated label fusion strategies have been proposed, DL approaches generally outperform MAS. In this work, we propose a DL-based label fusion strategy (VoteNet) which locally selects a set of reliable…
National Institutes of Health（美国国立卫生研究院）；University of California, Irvine School of Medicine（加州大学欧文分校医学院）
The precise and accurate segmentation of the vertebral column is essential in the diagnosis and treatment of various orthopedic, neurological, and oncological traumas and pathologies. Segmentation is especially challenging in the presence of pathology such as vertebral compression fractures. In this paper, we propose a method to produce segmentations for osteoporotic compression fractured vertebrae by applying a multi-atlas joint label fusion technique for clinical CT images. A total of 170 thoracic and lumbar vertebrae were evaluated using atlases from five patients with varying degrees of sp…
Single-photon emission computed tomography (SPECT) is a widely applied imaging approach for diagnosis of coronary artery diseases. Attenuation maps (u-maps) derived from computed tomography (CT) are utilized for attenuation correction (AC) to improve diagnostic accuracy of cardiac SPECT. However, SPECT and CT are obtained sequentially in clinical practice, which potentially induces misregistration between the two scans. Convolutional neural networks (CNN) are powerful tools for medical image registration. Previous CNN-based methods for cross-modality registration either directly concatenated t…
Fuzhou University（福州大学）；Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；King’s College London（伦敦国王学院）
Both image registration and label fusion in the multi-atlas segmentation (MAS) rely on the intensity similarity between target and atlas images. However, such similarity can be problematic when target and atlas images are acquired using different imaging protocols. High-level structure information can provide reliable similarity measurement for cross-modality images when cooperating with deep neural networks (DNNs). This work presents a new MAS framework for cross-modality images, where both image registration and label fusion are achieved by DNNs. For image registration, we propose a consiste…
Point cloud-based registration and image fusion between cardiac SPECT MPI and CTA
Xi’an University of Posts and Telecommunications（西安邮电大学）；Nanjing Medical University（南京医科大学）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）；The First Affiliated Hospital of Xi’an Jiaotong University（西安交通大学第一附属医院）；Kennesaw State University（肯尼索州立大学）；Michigan Technological University（密歇根理工大学）
ConvFormer3D-TAP: Phase/Uncertainty-Aware Front-End Fusion for Cine CMR View Classification Pipelines
Center for Artificial Intelligence, Bluhm Cardiovascular Institute, Northwestern Medicine（西北医学布鲁姆心血管研究所人工智能中心）；Department of Electrical and Computer Engineering, Northwestern University（西北大学电气与计算机工程系）；Department of Surgery, Northwestern University（西北大学外科学系）；Department of Radiology, Northwestern University（西北大学放射学系）；Xtasis Inc.（Xtasis公司）；Department of Cardiology, The Prince Charles Hospital（查尔斯王子医院心脏病科）；School of Medicine, The University of Queensland（昆士兰大学医学院）；Quantiphi Inc.（Quantiphi公司）
Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability of fully annotated atlas images for training is limited due to the time required for the labelling task. Segmentation methods requiring only a proportion of each atlas image to be labelled could therefore reduce the workload on expert raters tasked with annotating atlas images. To address this issue, we first re-examine the labelling problem common in many existing approaches and formulate its solution in terms of a…
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；University of Southern California（南加州大学）
Cardiac cine magnetic resonance imaging (MRI) has been used to characterize cardiovascular diseases (CVD), often providing a noninvasive phenotyping tool.~While recently flourished deep learning based approaches using cine MRI yield accurate characterization results, the performance is often degraded by small training samples. In addition, many deep learning models are deemed a ``black box," for which models remain largely elusive in how models yield a prediction and how reliable they are. To alleviate this, this work proposes a lightweight successive subspace learning (SSL) framework for CVD …
Reconstruction of Cardiac Cine MRI Using Motion-Guided Deformable Alignment and Multi-Resolution Fusion
School of Health Science and Engineering, University of Shanghai for Science and Technology（上海理工大学健康科学与工程学院）；ToolSensing Technologies Co., Ltd（拓深科技有限公司）；School of Medical Instruments, Shanghai University of Medicine and Health Sciences（上海健康医学院医疗器械学院）；Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University（华东师范大学通信与电子工程学院多维信息处理上海重点实验室）
Fudan University（复旦大学）；Leiden University Medical Center（莱顿大学医学中心）
Quantitative assessment of left ventricle (LV) function from cine MRI has significant diagnostic and prognostic value for cardiovascular disease patients. The temporal movement of LV provides essential information on the contracting/relaxing pattern of heart, which is keenly evaluated by clinical experts in clinical practice. Inspired by the expert way of viewing Cine MRI, we propose a new CNN module that is able to incorporate the temporal information into LV segmentation from cine MRI. In the proposed CNN, the optical flow (OF) between neighboring frames is integrated and aggregated at featu…
multimodars: A Rust-powered toolkit for multi-modality cardiac image fusion and registration
Department of Cardiology, Inselspital, Bern University Hospital, University of Bern（伯尔尼大学伯尔尼大学医院 Inselspital 心脏病学系）；Graduate School for Cellular and Biomedical Sciences, University of Bern（伯尔尼大学细胞与生物医学科学研究生院）
Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation. Generally, MAS methods register multiple atlases, i.e., medical images with corresponding labels, to a target image; and the transformed atlas labels can be combined to generate target segmentation via label fusion schemes. Many conventional MAS methods employed the atlases from the same modality as the target image. However, the number of atlases with the same modality may be limited or even missing in many clinical applications. Besides, conventional MAS methods suffer from the computational burden of reg…
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属协和医院放射科）；Hubei Province Key Laboratory of Molecular Imaging（湖北省分子影像重点实验室）；HUST-HW Joint Innovation Lab（华中科技大学-华为联合创新实验室）
Cardiac MRI segmentation plays a crucial role in clinical diagnosis for evaluating personalized cardiac performance parameters. Due to the indistinct boundaries and heterogeneous intensity distributions in the cardiac MRI, most existing methods still suffer from two aspects of challenges: inter-class indistinction and intra-class inconsistency. To tackle these two problems, we propose a novel method to exploit the directional feature maps, which can simultaneously strengthen the differences between classes and the similarities within classes. Specifically, we perform cardiac segmentation and l…
Uncertainty Quantification for Cardiac Shape Reconstruction with Deep Signed Distance Functions via MCMC methods
Department of Mathematics and Scientific Computing, University of Graz, Austria（格拉茨大学数学与科学计算系，奥地利）；PRIN-PNRR project no. P2022N5ZNP（PRIN-PNRR项目编号P2022N5ZNP）；INdAM-GNCS
Contrast-Free Myocardial Scar Segmentation in Cine MRI using Motion and Texture Fusion
University of Oxford（牛津大学）；University of Southampton（南安普顿大学）；National University of Singapore（新加坡国立大学）；Fudan University（复旦大学）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）
Department of Mechanical Engineering, MIT（麻省理工学院机械工程系）；Institute for Medical Engineering and Science, MIT（麻省理工学院医学工程与科学研究所）；MIT.nano Immersion Lab, MIT（麻省理工学院MIT.nano沉浸实验室）
Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusion
College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）；Saw Swee Hock School of Public Health at National University of Singapore（新加坡国立大学 Saw Swee Hock 公共卫生学院）；Cognitive Neuroimaging Centre and Lee Kong Chian School of Medicine, Nanyang Technological University（认知神经影像中心和 Lee Kong Chian 医学院，南洋理工大学）
Convolutional networks (ConvNets) have achieved promising accuracy for various anatomical segmentation tasks. Despite the success, these methods can be sensitive to data appearance variations. Considering the large variability of scans caused by artifacts, pathologies, and scanning setups, robust ConvNets are vital for clinical applications, while have not been fully explored. In this paper, we propose to mitigate the challenge by enabling ConvNets' awareness of the underlying anatomical invariances among imaging scans. Specifically, we introduce a fully convolutional Constraint Adoption Modul…
SAMba-UNet: SAM2-Mamba UNet for Cardiac MRI in Medical Robotic Perception
University of Electronic Science and Technology of China（电子科技大学）；University of Chinese Academy of Sciences（中国科学院大学）；School of Computer Science, Peking University（北京大学计算机学院）
Deep learning models for semantic segmentation are able to learn powerful representations for pixel-wise predictions, but are sensitive to noise at test time and do not guarantee a plausible topology. Image registration models on the other hand are able to warp known topologies to target images as a means of segmentation, but typically require large amounts of training data, and have not widely been benchmarked against pixel-wise segmentation models. We propose Atlas-ISTN, a framework that jointly learns segmentation and registration on 2D and 3D image data, and constructs a population-derived…
University of Pennsylvania（宾夕法尼亚大学）；Lund University（隆德大学）
Deep learning (DL) is the state-of-the-art methodology in various medical image segmentation tasks. However, it requires relatively large amounts of manually labeled training data, which may be infeasible to generate in some applications. In addition, DL methods have relatively poor generalizability to out-of-sample data. Multi-atlas segmentation (MAS), on the other hand, has promising performance using limited amounts of training data and good generalizability. A hybrid method that integrates the high accuracy of DL and good generalizability of MAS is highly desired and could play an importan…
Harbin Institute of Technology（哈尔滨工业大学）；The Fourth Hospital of Harbin Medical University（哈尔滨医科大学附属第四医院）；Western University（西安大略大学）；University of Manchester（曼彻斯特大学）；Digital Image Group (DIG)（数字图像组(DIG)）
3D left ventricle (LV) segmentation on echocardiography is very important for diagnosis and treatment of cardiac disease. It is not only because of that echocardiography is a real-time imaging technology and widespread in clinical application, but also because of that LV segmentation on 3D echocardiography can provide more full volume information of heart than LV segmentation on 2D echocardiography. However, 3D LV segmentation on echocardiography is still an open and challenging task owing to the lower contrast, higher noise and data dimensionality, limited annotation of 3D echocardiography. I…
College of Physics and Information Engineering, Fuzhou University（福州大学物理与信息工程学院）；School of Basic Medical Science, Fudan University（复旦大学基础医学院）
Multi-sequence of cardiac magnetic resonance (CMR) images can provide complementary information for myocardial pathology (scar and edema). However, it is still challenging to fuse these underlying information for pathology segmentation effectively. This work presents an automatic cascade pathology segmentation framework based on multi-modality CMR images. It mainly consists of two neural networks: an anatomical structure segmentation network (ASSN) and a pathological region segmentation network (PRSN). Specifically, the ASSN aims to segment the anatomical structure where the pathology may exis…
Royal Brompton Hospital（皇家布朗普顿医院）；National Heart and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；University of Lincoln（林肯大学）；City University London（伦敦城市大学）
Purpose: Atrial fibrillation (AF) is the most common cardiac arrhythmia and is correlated with increased morbidity and mortality. It is associated with atrial fibrosis, which may be assessed non-invasively using late gadolinium-enhanced (LGE) magnetic resonance imaging (MRI) where scar tissue is visualised as a region of signal enhancement. In this study, we proposed a novel fully automatic pipeline to achieve an accurate and objective atrial scarring segmentation and assessment of LGE MRI scans for the AF patients. Methods: Our fully automatic pipeline uniquely combined: (1) a multi-atlas bas…
Resource-Aware Evolutionary Neural Architecture Search for Cardiac MRI Segmentation
School of Computer Science, Nanjing University of Information Science and Technology（南京信息工程大学计算机科学学院）；School of Software, Nanjing University of Information Science and Technology（南京信息工程大学软件学院）；Eastern University（东东大学）；Research Centre for Intelligent Healthcare, Coventry University（科文大学智能医疗研究中心）；National Medical Research Association（国家医学研究协会）；Faculty of Engineering and Technology, Muscat University（穆斯cat大学工程与技术学院）；College of Computer and Information Sciences, King Saud University（沙特国王大学计算机与信息学院）
National Heart & Lung Institute, Imperial College London（伦敦帝国理工学院国家心肺研究所）；Royal Brompton Hospital（皇家布朗普顿医院）
Four-dimensional (4D) left ventricular myocardial velocity mapping (MVM) is a cardiac magnetic resonance (CMR) technique that allows assessment of cardiac motion in three orthogonal directions. Accurate and reproducible delineation of the myocardium is crucial for accurate analysis of peak systolic and diastolic myocardial velocities. In addition to the conventionally available magnitude CMR data, 4D MVM also acquires three velocity-encoded phase datasets which are used to generate velocity maps. These can be used to facilitate and improve myocardial delineation. Based on the success of deep l…
University of Pisa（比萨大学）；Fondazione Gabriele Monasterio（加布里埃莱·莫纳斯特里奥基金会）
Coronary artery calcium (CAC) is a significant marker of atherosclerosis and cardiovascular events. In this work we present a system for the automatic quantification of calcium score in ECG-triggered non-contrast enhanced cardiac computed tomography (CT) images. The proposed system uses a supervised deep learning algorithm, i.e. convolutional neural network (CNN) for the segmentation and classification of candidate lesions as coronary or not, previously extracted in the region of the heart using a cardiac atlas. We trained our network with 45 CT volumes; 18 volumes were used to validate the mo…
University of Oxford（牛津大学）；Fuzhou University（福州大学）；Fudan University（复旦大学）
Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optima…
Zhengzhou University of Light Industry（郑州轻工业大学）；University of Southern Mississippi（南密西西比大学）；Michigan Technological University（密歇根理工大学）；New York Presbyterian Queens Hospital（纽约长老会皇后医院）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）
Automatic identification of proper image frames at the end-diastolic (ED) and end-systolic (ES) frames during the review of invasive coronary angiograms (ICA) is important to assess blood flow during a cardiac cycle, reconstruct the 3D arterial anatomy from bi-planar views, and generate the complementary fusion map with myocardial images. The current identification method primarily relies on visual interpretation, making it not only time-consuming but also less reproducible. In this paper, we propose a new method to automatically identify angiographic image frames associated with the ED and ES…
Center for Research in Computer Vision (CRCV), University of Central Florida（中佛罗里达大学计算机视觉研究中心）；Diagnostic Radiology Department, Florida Hospital（佛罗里达医院放射诊断科）
Non-invasive detection of cardiovascular disorders from radiology scans requires quantitative image analysis of the heart and its substructures. There are well-established measurements that radiologists use for diseases assessment such as ejection fraction, volume of four chambers, and myocardium mass. These measurements are derived as outcomes of precise segmentation of the heart and its substructures. The aim of this paper is to provide such measurements through an accurate image segmentation algorithm that automatically delineates seven substructures of the heart from MRI and/or CT scans. O…
Combining information from multi-view images is crucial to improve the performance and robustness of automated methods for disease diagnosis. However, due to the non-alignment characteristics of multi-view images, building correlation and data fusion across views largely remain an open problem. In this study, we present TransFusion, a Transformer-based architecture to merge divergent multi-view imaging information using convolutional layers and powerful attention mechanisms. In particular, the Divergent Fusion Attention (DiFA) module is proposed for rich cross-view context modeling and semanti…
White Matter Hyperintensities (WMH) are the most common manifestation of cerebral small vessel disease (cSVD) on the brain MRI. Accurate WMH segmentation algorithms are important to determine cSVD burden and its clinical consequences. Most of existing WMH segmentation algorithms require both fluid attenuated inversion recovery (FLAIR) images and T1-weighted images as inputs. However, T1-weighted images are typically not part of standard clinicalscans which are acquired for patients with acute stroke. In this paper, we propose a novel brain atlas guided attention U-Net (BAGAU-Net) that leverage…
The presence of missing entries in data often creates challenges for pattern recognition algorithms. Traditional algorithms for clustering data assume that all the feature values are known for every data point. We propose a method to cluster data in the presence of missing information. Unlike conventional clustering techniques where every feature is known for each point, our algorithm can handle cases where a few feature values are unknown for every point. For this more challenging problem, we provide theoretical guarantees for clustering using a $\ell_0$ fusion penalty based optimization prob…
