Catheter based radiofrequency ablation for pulmonary vein isolation has become the first line of treatment for atrial fibrillation in recent years. This requires a rather accurate map of the left atrial sub-endocardial surface including the ostia of the pulmonary veins, which requires dense sampling of the surface and takes more than 10 minutes. The focus of this work is to provide left atrial visualization early in the procedure to ease procedure complexity and enable further workflows, such as using catheters that have difficulty sampling the surface. We propose a dense encoder-decoder network with a novel regularization term to reconstruct the shape of the left atrium from partial data which is derived from simple catheter maneuvers. To train the network, we acquire a large dataset of 3D atria shapes and generate corresponding catheter trajectories. Once trained, we show that the suggested network can sufficiently approximate the atrium shape based on a given trajectory. We compare several network solutions for the 3D atrium reconstruction. We demonstrate that the solution proposed produces realistic visualization using partial acquisition within a 3-minute time interval. Synthetic and human clinical cases are shown.
Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局资讯通信研究院）；Nanyang Technological University（南洋理工大学）
Deep learning has achieved promising segmentation performance on 3D left atrium MR images. However, annotations for segmentation tasks are expensive, costly and difficult to obtain. In this paper, we introduce a novel hierarchical consistency regularized mean teacher framework for 3D left atrium segmentation. In each iteration, the student model is optimized by multi-scale deep supervision and hierarchical consistency regularization, concurrently. Extensive experiments have shown that our method achieves competitive performance as compared with full annotation, outperforming other state-of-the…
This paper presents a fully automated method to segment the complex left atrial (LA) cavity, from 3D Gadolinium-enhanced magnetic resonance imaging (GE-MRI) scans. The proposed method consists of four steps: (1) preprocessing to convert the original GE-MRI to a probability map, (2) atlas selection to match the atlases to the target image, (3) multi-atlas registration and fusion, and (4) level-set refinement. The method was evaluated on the datasets provided by the MICCAI 2018 STACOM Challenge with 100 dataset for training. Compared to manual annotation, the proposed method achieved an average …
Anhui University（安徽大学）；Royal Brompton Hospital（皇家布朗普顿医院）；National Heart & Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University College London（伦敦大学学院）；Alan Turing Institute（艾伦·图灵研究所）；Imperial College London（帝国理工学院）
Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in atrial fibrillation (AF) patients has recently emerged as a promising technique to stratify patients, guide ablation therapy and predict treatment success. Visualisation and quantification of scar tissues require a segmentation of both the left atrium (LA) and the high intensity scar regions from LGE-CMRI images. These two segmentation tasks are challenging due to the cancelling of healthy tissue signal, low signal-to-noise ratio and often limited image quality in these patients. Most approaches require manual superv…
University of California, Los Angeles（加州大学洛杉矶分校）；University of Alberta（阿尔伯塔大学）；Mazankowski Alberta Heart Institute（马赞科夫斯基阿尔伯塔心脏研究所）
This study proposes a fully automated approach for the left atrial segmentation from routine cine long-axis cardiac magnetic resonance image sequences using deep convolutional neural networks and Bayesian filtering. The proposed approach consists of a classification network that automatically detects the type of long-axis sequence and three different convolutional neural network models followed by unscented Kalman filtering (UKF) that delineates the left atrium. Instead of training and predicting all long-axis sequence types together, the proposed approach first identifies the image sequence t…
Cardiovascular Research Centre, Royal Brompton Hospital（皇家布朗普顿医院心血管研究中心）；National Heart and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；Anhui University（安徽大学）；Western University（西安大略大学）；University College London（伦敦大学学院）；Alan Turing Institute（阿兰·图灵研究所）；NIHR Imperial Biomedical Research Centre（NIHR帝国生物医学研究中心）；Beijing Anzhen Hospital, Capital Medical University（首都医科大学附属北京安贞医院）；School of Biomedical Engineering, Sun Yat-Sen University（中山大学医学院生物医学工程学院）
Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment success. This requires a segmentation of the high intensity scar tissue and also a segmentation of the left atrium (LA) anatomy, the latter usually being derived from a separate bright-blood acquisition. Performing both segmentations automatically from a single 3D LGE CMR acquisition would eliminate the need for an additional acquisition and avoid sub…
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）；Technical University Munich（慕尼黑工业大学）；Helmholtz Center Munich（慕尼黑亥姆霍兹中心）
Left atrial (LA) and atrial scar segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is an important task in clinical practice. %, to guide ablation therapy and predict treatment results for atrial fibrillation (AF) patients. The automatic segmentation is however still challenging, due to the poor image quality, the various LA shapes, the thin wall, and the surrounding enhanced regions. Previous methods normally solved the two tasks independently and ignored the intrinsic spatial relationship between LA and scars. In this work, we develop a new framework, namely Atr…
Sun Yat-sen University（中山大学）；Royal Brompton Hospital（皇家布朗普顿医院）；Imperial College London（帝国理工学院）
Semi-supervised learning provides great significance in left atrium (LA) segmentation model learning with insufficient labelled data. Generalising semi-supervised learning to cross-domain data is of high importance to further improve model robustness. However, the widely existing distribution difference and sample mismatch between different data domains hinder the generalisation of semi-supervised learning. In this study, we alleviate these problems by proposing an Adaptive Hierarchical Dual Consistency (AHDC) for the semi-supervised LA segmentation on cross-domain data. The AHDC mainly consis…
Coronary computed tomography angiography (CCTA) provides detailed an-atomical information on all chambers of the heart. Existing segmentation tools can label the gross anatomy, but addition of application-specific labels can require detailed and often manual refinement. We developed a U-Net based framework to i) extrapolate a new label from existing labels, and ii) parcellate one label into multiple labels, both using label-to-label mapping, to create a desired segmentation that could then be learnt directly from the image (image- to-label mapping). This approach only required manual correctio…
Sun Yat-sen University（中山大学）；Royal Brompton Hospital（皇家布朗普顿医院）；Imperial College London（帝国理工学院）
Automated and accurate segmentations of left atrium (LA) and atrial scars from late gadolinium-enhanced cardiac magnetic resonance (LGE CMR) images are in high demand for quantifying atrial scars. The previous quantification of atrial scars relies on a two-phase segmentation for LA and atrial scars due to their large volume difference (unbalanced atrial targets). In this paper, we propose an inter-cascade generative adversarial network, namely JAS-GAN, to segment the unbalanced atrial targets from LGE CMR images automatically and accurately in an end-to-end way. Firstly, JAS-GAN investigates a…
Atrial fibrillation is a heart rhythm disorder that affects tens of millions people worldwide. The most effective treatment is catheter ablation. This involves irreversible heating of abnormal cardiac tissue facilitated by electroanatomical mapping. However, it is difficult to consistently identify the triggers and sources that may initiate or perpetuate atrial fibrillation due to its chaotic behavior. We developed a patient-specific computational heart model that can accurately reproduce the activation patterns to help in localizing these triggers and sources. Our model has high spatial resol…
LA-CaRe-CNN: Cascading Refinement CNN for Left Atrial Scar Segmentation
Gottfried Schatz Research Center: Medical Physics and Biophysics（戈特弗里德·施蔡茨研究所以及生物物理学）；Medical University of Graz（格拉茨医科大学）；Institute of Computer Graphics and Vision（计算机图形学与视觉研究所）；Graz University of Technology（格拉茨理工大学）；AVL List GmbH；Institute for Medical Informatics, Statistics and Documentation（医学信息学、统计学与文档研究所）
Two-Stage Deep Learning Framework for Quality Assessment of Left Atrial Late Gadolinium Enhanced MRI Images
Kahlert School of Computing, University of Utah（犹他大学卡勒特计算学院）；Department of Biomedical Engineering, University of Utah（犹他大学生物医学工程系）；Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah（犹他大学诺拉·埃克尔斯·哈里森心血管研究与培训研究所）；Department of Biomedical Engineering, Johns Hopkins（约翰斯·霍普金斯大学生物医学工程系）；Division of Cardiology, University of Utah（犹他大学心脏病学系）；Department of Radiology and Imaging Sciences, University of Utah（犹他大学放射学与影像科学系）；Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）
Auckland Bioengineering Institute, University of Auckland（奥克兰大学生物工程研究所）；Beihang University（北京航空航天大学）；Peking University（北京大学）；SenseTime Inc（商汤科技）；Tencent Jarvis Laboratory（腾讯Jarvis实验室）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen University（深圳大学）；Wuhan University（武汉大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；EPITA Research and Development Laboratory（EPITA研发实验室）；Imperial College London（伦敦帝国学院）；Shanghai Jiao Tong University（上海交通大学）；Fudan University（复旦大学）；Harbin Institute of Technology（哈尔滨工业大学）；University of Bologna（博洛尼亚大学）；Eindhoven University of Technology（埃因霍温理工大学）；Kings College London（伦敦国王学院）；University of Magdeburg（马格德堡大学）；Heidelberg University Hospital（海德堡大学医院）；Leiden University Medical Center（莱顿大学医学中心）；Universitat Pompeu Fabra（庞培法布拉大学）；Physense（Physense机构）
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation of LGE-MRIs is challenging due to its attenuated contrast. Since most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning approaches. To address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the…
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, characterised by a rapid and irregular electrical activation of the atria. Treatments for AF are often ineffective and few atrial biomarkers exist to automatically characterise atrial function and aid in treatment selection for AF. Clinical metrics of left atrial (LA) function, such as ejection fraction (EF) and active atrial contraction ejection fraction (aEF), are promising, but have until now typically relied on volume estimations extrapolated from single-slice images. In this work, we study volumetric functional biom…
