In the time-series analysis, the time series motifs and the order patterns in time series can reveal general temporal patterns and dynamic features. Triadic Motif Field (TMF) is a simple and effective time-series image encoding method based on triadic time series motifs. Electrocardiography (ECG) signals are time-series data widely used to diagnose various cardiac anomalies. The TMF images contain the features characterizing the normal and Atrial Fibrillation (AF) ECG signals. Considering the quasi-periodic characteristics of ECG signals, the dynamic features can be extracted from the TMF images with the transfer learning pre-trained convolutional neural network (CNN) models. With the extracted features, the simple classifiers, such as the Multi-Layer Perceptron (MLP), the logistic regression, and the random forest, can be applied for accurate anomaly detection. With the test dataset of the PhysioNet Challenge 2017 database, the TMF classification model with the VGG16 transfer learning model and MLP classifier demonstrates the best performance with the 95.50% ROC-AUC and 88.43% F1 score in the AF classification. Besides, the TMF classification model can identify AF patients in the test dataset with high precision. The feature vectors extracted from the TMF images show clear patient-wise clustering with the t-distributed Stochastic Neighbor Embedding technique. Above all, the TMF classification model has very good clinical interpretability. The patterns revealed by symmetrized Gradient-weighted Class Activation Mapping have a clear clinical interpretation at the beat and rhythm levels.
Atrial fibrillation(termed as AF/Afib henceforth) is a discrete and often rapid heart rhythm that can lead to clots near the heart. We can detect Afib by ECG signal by the absence of p and inconsistent intervals between R waves as shown in fig(1). Existing methods revolve around CNN that are used to detect afib but most of them work with 12 point lead ECG data where in our case the health gauge watch deals with single-point ECG data. Twelve-point lead ECG data is more accurate than a single point. Furthermore, the health gauge watch data is much noisier. Implementing a model to detect Afib for…
It is challenging to visually detect heart disease from the electrocardiographic (ECG) signals. Implementing an automated ECG signal detection system can help diagnosis arrhythmia in order to improve the accuracy of diagnosis. In this paper, we proposed, implemented, and compared an automated system using two different frameworks of the combination of convolutional neural network (CNN) and long-short term memory (LSTM) for classifying normal sinus signals, atrial fibrillation, and other noisy signals. The dataset we used is from the MIT-BIT Arrhythmia Physionet. Our approach demonstrated that …
National University of Singapore（新加坡国立大学）；University College Dublin（都柏林大学学院）
Deep neural networks (DNN) are a promising tool in medical applications. However, the implementation of complex DNNs on battery-powered devices is challenging due to high energy costs for communication. In this work, a convolutional neural network model is developed for detecting atrial fibrillation from electrocardiogram (ECG) signals. The model demonstrates high performance despite being trained on limited, variable-length input data. Weight pruning and logarithmic quantisation are combined to introduce sparsity and reduce model size, which can be exploited for reduced data movement and lowe…
The University of Virginia’s College at Wise（弗吉尼亚大学怀斯学院）；The University of North Carolina at Wilmington（北卡罗来纳大学威尔明顿分校）
Atrial Fibrillation is a common form of irregular heart rhythm that can be very dangerous. Our primary goal is to analyze Atrial Fibrillation data within ECGs to develop a model based only on RR-Intervals, or the length between heart-beats, to create a real time classification model for Atrial Fibrillation to be implemented in common heart-rate monitors on the market today. Physionet's MIT-BIH Atrial Fibrillation Database \cite{goldberger2000physiobank} and 2017 Challenge Database \cite{clifford2017af} were used to identify patterns of Atrial Fibrillation and test classification models on. The…
Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；United International University（联合国际大学）；University of Maryland（马里兰大学）；United Arab Emirates University（阿联酋大学）
Tarbiat Modares University（塔比亚特·莫达雷斯大学）；Shiraz University of Medical Science（设拉子医科大学）；Mälardalen University（梅拉达伦大学）；Halmstad University（哈尔姆斯塔德大学）
This paper presents a novel machine learning framework for detecting Paroxysmal Atrial Fibrillation (PxAF), a pathological characteristic of Electrocardiogram (ECG) that can lead to fatal conditions such as heart attack. To enhance the learning process, the framework involves a Generative Adversarial Network (GAN) along with a Neural Architecture Search (NAS) in the data preparation and classifier optimization phases. The GAN is innovatively invoked to overcome the class imbalance of the training data by producing the synthetic ECG for PxAF class in a certified manner. The effect of the certif…
Università della Svizzera italiana（提契诺大学）；University of Pennsylvania（宾夕法尼亚大学）；Pontificia Universidad Católica de Chile（智利天主教大学）
Computational models of atrial fibrillation have successfully been used to predict optimal ablation sites. A critical step to assess the effect of an ablation pattern is to pace the model from different, potentially random, locations to determine whether arrhythmias can be induced in the atria. In this work, we propose to use multi-fidelity Gaussian process classification on Riemannian manifolds to efficiently determine the regions in the atria where arrhythmias are inducible. We build a probabilistic classifier that operates directly on the atrial surface. We take advantage of lower resolutio…
Università della Svizzera italiana（瑞士意大利语大学）；UiT the Arctic University of Norway（挪威北极大学）；TU Wien（维也纳技术大学）；University of Exeter（埃克塞特大学）
We tackle the problem of classifying Electrocardiography (ECG) signals with the aim of predicting the onset of Paroxysmal Atrial Fibrillation (PAF). Atrial fibrillation is the most common type of arrhythmia, but in many cases PAF episodes are asymptomatic. Therefore, in order to help diagnosing PAF, it is important to design procedures for detecting and, more importantly, predicting PAF episodes. We propose a method for predicting PAF events whose first step consists of a feature extraction procedure that represents each ECG as a multi-variate time series. Successively, we design a classificat…
Azienda Sanitaria Universitaria Giuliano Isontina (ASUGI)（朱利亚诺·伊松蒂纳大学卫生机构（ASUGI））；University of Trieste（的里雅斯特大学）；Karolinska Institutet（卡罗林斯卡学院）；Area Science Park, Research and Technology Institute（区域科学园研究技术研究所）
Atrial Fibrillation (AF) is a heart's arrhythmia which, despite being often asymptomatic, represents an important risk factor for stroke, therefore being able to predict AF at the electrocardiogram exam, would be of great impact on actively targeting patients at high risk. In the present work we use Convolution Neural Networks to analyze ECG and predict Atrial Fibrillation starting from realistic datasets, i.e. considering fewer ECG than other studies and extending the maximal distance between ECG and AF diagnosis. We achieved 75.5% (0.75) AUC firstly increasing our dataset size by a shifting …
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Pennsylvania State University（宾夕法尼亚州立大学）；United International University（联合国际大学）；University of Rochester（罗切斯特大学）；United Arab Emirates University（阿联酋大学）
Smartwatches or fitness trackers have garnered a lot of popularity as potential health tracking devices due to their affordable and longitudinal monitoring capabilities. To further widen their health tracking capabilities, in recent years researchers have started to look into the possibility of Atrial Fibrillation (AF) detection in real-time leveraging photoplethysmography (PPG) data, an inexpensive sensor widely available in almost all smartwatches. A significant challenge in AF detection from PPG signals comes from the inherent noise in the smartwatch PPG signals. In this paper, we propose a…
Texas Cardiac Arrhythmia Institute（德克萨斯心律失常研究所）；St David’s Medical Center（圣大卫医疗中心）；BioSig Technologies Inc（BioSig科技公司）
Introduction: 12-lead electrocardiogram (ECG) is recorded during atrial fibrillation (AF) catheter ablation procedure (CAP). It is not easy to determine if CAP was successful without a long follow-up assessing for AF recurrence (AFR). Therefore, an AFR risk prediction algorithm could enable a better management of CAP patients. In this research, we extracted features from 12-lead ECG recorded before and after CAP and train an AFR risk prediction machine learning model. Methods: Pre- and post-CAP segments were extracted from 112 patients. The analysis included a signal quality criterion, heart r…
Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation
Shenzhen International Graduate School（深圳国际研究生院）；Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology (BNRist)（北京信息科学与技术国家研究中心）；Beijing Tsinghua Changgung Hospital（北京清华长庚医院）；School of Medicine（医学院）
针对 ECG 标注昂贵及房颤特征捕获不足的问题，提出融合 RR 间期与 P 波知识的周期间-周期内自监督表示学习方法，并在多个数据集取得优异房颤检测性能。
Memorial Antalya Hospital（安塔利亚纪念医院）；Notrino Research（Notrino研究）；Akdeniz University（阿克deniz大学）；Şanlıurfa Education And Research（尚勒乌尔法教育与研究中心）；Bolu Abant Izzet Baysal University（博卢阿邦特·伊兹·巴伊萨尔大学）
Electrocardiogram recognition of cardiac arrhythmias is critical for cardiac abnormality diagnosis. Because of their strong prediction characteristics, artificial neural networks are the preferred method in medical diagnosis systems. This study presents a method to detect atrial fibrillation with lead-I ECGs using artificial intelligence. The aim of the study is to compare the accuracy of the diagnoses estimated by cardiologists and artificial intelligence over lead-I ECGs using 12-lead ECGs as references. To evaluate the performance of the proposed model, dataset were collected from China Phy…
TU Graz（格拉茨技术大学）；BioTechMed-Graz（格拉茨生物技术医学中心）；Università della Svizzera italiana（瑞士意大利语大学）；Pontificia Universidad Católica de Chile（智利天主教大学）；University of Pennsylvania（宾夕法尼亚大学）；Medical University of Graz（格拉茨医科大学）
Electroanatomical maps are a key tool in the diagnosis and treatment of atrial fibrillation. Current approaches focus on the activation times recorded. However, more information can be extracted from the available data. The fibers in cardiac tissue conduct the electrical wave faster, and their direction could be inferred from activation times. In this work, we employ a recently developed approach, called physics informed neural networks, to learn the fiber orientations from electroanatomical maps, taking into account the physics of the electrical wave propagation. In particular, we train the n…
Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements
Imperial College London（帝国理工学院）；National Heart and Lung Institute（国家心肺研究所）；The Swiss AI Lab IDSIA USI-SUPSI（瑞士IDSIA USI-SUPSI人工智能实验室）；Università della Svizzera italiana（瑞士意大利语大学）；University of Oxford（牛津大学）；Oxford University Hospitals NHS Foundation Trust（牛津大学医院NHS基金会）；Politecnico di Milano（米兰理工大学）
Atrial Fibrillation (AF) is the most common type of arrhythmia (Greek a-, loss + rhythmos, rhythm = loss of rhythm) leading to hospitalization in the United States. Though sometimes AF is asymptomatic, it increases the risk of stroke and heart failure in patients, in addition to lowering the health-related quality of life (HRQOL). AF-related care costs the healthcare system between $6.0 to $26 billion each year. Early detection of AF and clinical attention can help improve symptoms and HRQOL of the patient, as well as bring down the cost of care. However, the prevalent paradigm of AF detection…
In this article, we propose a novel ECG classification framework for atrial fibrillation (AF) detection using spectro-temporal representation (i.e., time varying spectrum) and deep convolutional networks. In the first step we use a Bayesian spectro-temporal representation based on the estimation of time-varying coefficients of Fourier series using Kalman filter and smoother. Next, we derive an alternative model based on a stochastic oscillator differential equation to accelerate the estimation of the spectro-temporal representation in lengthy signals. Finally, after comparative evaluations of …
In this work we apply variations of ResNet architecture to the task of atrial fibrillation classification. Variations differ in number of filter after first convolution, ResNet block layout, number of filters in block convolutions and number of ResNet blocks between downsampling operations. We have found a range of model size in which models with quite different configurations show similar performance. It is likely that overall number of parameters plays dominant role in model performance. However, configuration parameters like layout have values that constantly lead to better results, which a…
We present a convolutional-recurrent neural network architecture with long short-term memory for real-time processing and classification of digital sensor data. The network implicitly performs typical signal processing tasks such as filtering and peak detection, and learns time-resolved embeddings of the input signal. We use a prototype multi-sensor wearable device to collect over 180h of photoplethysmography (PPG) data sampled at 20Hz, of which 36h are during atrial fibrillation (AFib). We use end-to-end learning to achieve state-of-the-art results in detecting AFib from raw PPG data. For cla…
The automatic detection of atrial fibrillation based on electrocardiograph (ECG) signals has received wide attention both clinically and practically. It is challenging to process ECG signals with cyclical pattern, varying length and unstable quality due to noise and distortion. Besides, there has been insufficient research on separating persistent atrial fibrillation from paroxysmal atrial fibrillation, and little discussion on locating the onsets and end points of AF episodes. It is even more arduous to perform well on these two distinct but interrelated tasks, while avoiding the mistakes inh…
SHDB-AF: a Japanese Holter ECG database of atrial fibrillation
Teikyo University School of Medicine（帝京大学医学部）；Saitama Medical University International Medical Center（埼玉医科大学国际医疗中心）；Technion-IIT（以色列理工学院）；Centre hospitalier Universitaire de Nancy（南锡大学医院）；INSERM（法国国家健康与医学研究院）；Universite de Lorraine（洛林大学）
Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）；School of Computing, National University of Singapore（新加坡国立大学计算学院）；National University of Singapore（新加坡国立大学）
Atrial Fibrillation (AF) is a common cardiac arrhythmia affecting a large number of people around the world. If left undetected, it will develop into chronic disability or even early mortality. However, patients who have this problem can barely feel its presence, especially in its early stage. A non-invasive, automatic, and effective detection method is therefore needed to help early detection so that medical intervention can be implemented in time to prevent its progression. Electrocardiogram (ECG), which records the electrical activities of the heart, has been widely used for detecting the p…
Fraunhofer Institute for Integrated Circuits IIS（弗劳恩霍夫集成电路研究所）；Friedrich-Alexander University Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；University of Leeds（利兹大学）
Cardiovascular diseases are the most common cause of mortality worldwide. Detection of atrial fibrillation (AF) in the asymptomatic stage can help prevent strokes. It also improves clinical decision making through the delivery of suitable treatment such as, anticoagulant therapy, in a timely manner. The clinical significance of such early detection of AF in electrocardiogram (ECG) signals has inspired numerous studies in recent years, of which many aim to solve this task by leveraging machine learning algorithms. ECG datasets containing AF samples, however, usually suffer from severe class imb…
Atrial fibrillation is a cardiac arrhythmia that affects an estimated 33.5 million people globally and is the potential cause of 1 in 3 strokes in people over the age of 60. Detection and diagnosis of atrial fibrillation (AFIB) is done noninvasively in the clinical environment through the evaluation of electrocardiograms (ECGs). Early research into automated methods for the detection of AFIB in ECG signals focused on traditional bio-medical signal analysis to extract important features for use in statistical classification models. Artificial intelligence models have more recently been used tha…
Indian Institute of Technology, Roorkee（鲁尔基印度理工学院）；Leicester Royal Infirmary（莱斯特皇家医院）；Glenfield Hospital（格伦菲尔德医院）
The incidences of atrial fibrillation (AFib) are increasing at a daunting rate worldwide. For the early detection of the risk of AFib, we have developed an automatic detection system based on deep neural networks. For achieving better classification, it is mandatory to have good pre-processing of physiological signals. Keeping this in mind, we have proposed a two-fold study. First, an end-to-end model is proposed to denoise the electrocardiogram signals using denoising autoencoders (DAE). To achieve denoising, we have used three networks including, convolutional neural network (CNN), dense neu…
Deciphering Heartbeat Signatures: A Vision Transformer Approach to Explainable Atrial Fibrillation Detection from ECG Signals
Walkky（沃基公司）；Chobanian & Avedisian School of Medicine, Boston University（波士顿大学乔巴尼亚与阿夫迪西安医学院）；Clemson University（克莱姆森大学）；Northwell Health Physician Partners（诺斯韦尔健康医师伙伴）；Lenox Hill Hospital（莱诺克斯山医院）
Northern Arizona University（北亚利桑那大学）；Ngee Ann Polytechnic（义安理工学院）
Atrial fibrillation (AF) is one of the most prevalent cardiac arrhythmias that affects the lives of more than 3 million people in the U.S. and over 33 million people around the world and is associated with a five-fold increased risk of stroke and mortality. like other problems in healthcare domain, artificial intelligence (AI)-based algorithms have been used to reliably detect AF from patients' physiological signals. The cardiologist level performance in detecting this arrhythmia is often achieved by deep learning-based methods, however, they suffer from the lack of interpretability. In other …
Scientific Computing and Imaging Institute（科学计算与成像研究所）；Comprehensive Arrhythmia Research and Management Center（综合性心律失常研究与管理中心）
Left atrium shape has been shown to be an independent predictor of recurrence after atrial fibrillation (AF) ablation. Shape-based representation is imperative to such an estimation process, where correspondence-based representation offers the most flexibility and ease-of-computation for population-level shape statistics. Nonetheless, population-level shape representations in the form of image segmentation and correspondence models derived from cardiac MRI require significant human resources with sufficient anatomy-specific expertise. In this paper, we propose a machine learning approach that …
Many types of ventricular and atrial cardiac arrhythmias have been discovered in clinical practice in the past 100 years, and these arrhythmias are a major contributor to sudden cardiac death. Ventricular tachycardia, ventricular fibrillation, and paroxysmal atrial fibrillation are the most commonly-occurring and dangerous arrhythmias, therefore early detection is crucial to prevent any further complications and reduce fatalities. Implantable devices such as pacemakers are commonly used in patients at high risk of sudden cardiac death. While great advances have been made in medical technology,…
Atrial Fibrillation Detection with Arbitrary Leads via a Codebook-Based Reconstruction-Classification Framework
Information Center, The People’s Hospital of Baoan Shenzhen（深圳市宝安区人民医院信息中心）；School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）；Department of Cardiology, The People’s Hospital of Baoan Shenzhen（深圳市宝安区人民医院心内科）；Institute for Advanced Study, Shenzhen University（深圳大学高等研究院）
Technion-IIT（以色列理工学院）；Centre hospitalier Universitaire de Nancy（南锡大学医院中心）；Rambam Medical Center（兰巴姆医疗中心）；Technion The Ruth and Bruce Rappaport Faculty of Medicine（以色列理工学院露丝和布鲁斯·拉帕波特医学院）；Saitama Medical University International Medical Center（埼玉医科大学国际医疗中心）；Mines Paris, PSL Research University（巴黎矿业学院，巴黎文理研究大学）；INSERM（法国国家健康与医学研究院）；Universite de Lorraine（洛林大学）
To drive health innovation that meets the needs of all and democratize healthcare, there is a need to assess the generalization performance of deep learning (DL) algorithms across various distribution shifts to ensure that these algorithms are robust. This retrospective study is, to the best of our knowledge, the first to develop and assess the generalization performance of a deep learning (DL) model for AF events detection from long term beat-to-beat intervals across ethnicities, ages and sexes. The new recurrent DL model, denoted ArNet2, was developed on a large retrospective dataset of 2,14…
University of Delaware（特拉华大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Taipei Veterans General Hospital（台北荣民总医院）；National Yang-Ming University（阳明大学）；University of California San Francisco（加利福尼亚大学旧金山分校）
Hypertrophic cardiomyopathy (HCM) patients have a high incidence of atrial fibrillation (AF) and increased stroke risk, even with low risk of congestive heart failure, hypertension, age, diabetes, previous stroke/transient ischemic attack scores. Hence, there is a need to understand the pathophysiology of AF and stroke in HCM. In this retrospective study, we develop and apply a data-driven, machine learning based method to identify AF cases, and clinical and imaging features associated with AF, using electronic health record data. HCM patients with documented paroxysmal/persistent/permanent AF…
Clinically Interpretable Mortality Prediction for ICU Patients with Diabetes and Atrial Fibrillation: A Machine Learning Approach
organization= Daniel J. Epstein Department of Industrial \& Systems Engineering, University of Southern California , city= Los Angeles , state= CA , postcode= 90089 , country= United States；organization= Colorectal Research Center, Iran University of Medical Sciences , city= Tehran , postcode= 14535 , country= Iran；organization= Department of Health Science, California State University , city= Long Beach , state= CA , postcode= 90840 , country= United States；organization= Department of Industrial；Manufacturing Engineering, California State Polytechnic University , city= Pomona , state= CA , postcode= 91768 , country= United States
AcousAF: Acoustic Sensing-Based Atrial Fibrillation Detection System for Mobile Phones
Research Institute of Trustworthy Autonomous Systems（可信自主系统研究院）；Southern University of Science and Technology（南方科技大学）；Southern University of Science and Technology Hospital（南方科技大学医院）
The University of Western Australia（西澳大利亚大学）；Harry Perkins Institute of Medical Research（哈里·珀金斯医学研究所）；Sir Charles Gairdner Hospital（查尔斯·盖尔德纳爵士医院）；Murdoch University（默多克大学）；University of Ottawa Heart Institute（渥太华大学心脏研究所）；Fiona Stanley Hospital（菲奥娜·斯坦利医院）；University of Ottawa（渥太华大学）；Curtin University（科廷大学）
Appropriate antithrombotic therapy for patients with atrial fibrillation (AF) requires assessment of ischemic stroke and bleeding risks. However, risk stratification schemas such as CHA2DS2-VASc and HAS-BLED have modest predictive capacity for patients with AF. Machine learning (ML) techniques may improve predictive performance and support decision-making for appropriate antithrombotic therapy. We compared the performance of multilabel ML models with the currently used risk scores for predicting outcomes in AF patients. Materials and Methods This was a retrospective cohort study of 9670 patien…
A Dataset and Benchmarks for Atrial Fibrillation Detection from Electrocardiograms of Intensive Care Unit Patients
Department of Electrical and Computer Engineering（电气与计算机工程系）；Queen’s University（女王大学）；School of Computing（计算机学院）；Smith School of Business（商学院）；Departments of Emergency Medicine and Critical Care Medicine（急诊医学与重症医学系）；School of Computing and the Department of Electrical and Computer Engineering（计算机学院和电气与计算机工程系）；Department of Surgery（外科系）；Departments of Medicine and Critical Care Medicine（医学与重症医学系）；University of British Columbia（不列颠哥伦比亚大学）
SSN College of Engineering（SSN工程学院）；Solarillion Foundation（Solarillion基金会）
Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide, with 2% of the population affected. It is associated with an increased risk of strokes, heart failure and other heart-related complications. Monitoring at-risk individuals and detecting asymptomatic AF could result in considerable public health benefits, as individuals with asymptomatic AF could take preventive measures with lifestyle changes. With increasing affordability to wearables, personalized health care is becoming more accessible. These personalized healthcare solutions require accurate classification of bio-…
Pre-AF 13: An Interpretable Atrial Fibrillation Risk Score Mined from Discharge Reports
National Medical Research Center of Cardiology named after Academician E.I. Chazov（国家医学研究中心心脏病学以E.I. Chazov院士命名）；Skolkovo Institute of Science and Technology (Skoltech)（斯科尔科沃科学技术研究所）；Artificial Intelligence Research Institute (AIRI)（人工智能研究所）；University of Mannheim（曼海姆大学）；Russian Center for Scientific Information (RCSI)（俄罗斯科学信息中心）；Institute of Cyber Intelligence Systems, National Research Nuclear University MEPhI（网络智能系统研究所，国家研究核大学MEPhI）；M.V. Lomonosov Moscow State University（莫斯科国立罗蒙诺索夫大学）；Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute)（俄罗斯科学院信息传输问题研究所（Kharkevich研究所））；Ivannikov Institute for System Programming of the Russian Academy of Sciences (ISP RAS)（俄罗斯科学院伊万尼科夫系统编程研究所）；Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences (FRC CSC RAS)（俄罗斯科学院联邦研究中心“计算机科学与控制”）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）
Fuzhou University（福州大学）；College of Physics and Information Engineering, Fuzhou University（福州大学物理与信息工程学院）
Automatic segmentation of left atrial (LA) scars from late gadolinium enhanced CMR images is a crucial step for atrial fibrillation (AF) recurrence analysis. However, delineating LA scars is tedious and error-prone due to the variation of scar shapes. In this work, we propose a boundary-aware LA scar segmentation network, which is composed of two branches to segment LA and LA scars, respectively. We explore the inherent spatial relationship between LA and LA scars. By introducing a Sobel fusion module between the two segmentation branches, the spatial information of LA boundaries can be propag…
