Echocardiogram (echo) is the earliest and the primary tool for identifying regional wall motion abnormalities (RWMA) in order to diagnose myocardial infarction (MI) or commonly known as heart attack. This paper proposes a novel approach, Active Polynomials, which can accurately and robustly estimate the global motion of the Left Ventricular (LV) wall from any echo in a robust and accurate way. The proposed algorithm quantifies the true wall motion occurring in LV wall segments so as to assist cardiologists diagnose early signs of an acute MI. It further enables medical experts to gain an enhanced visualization capability of echo images through color-coded segments along with their "maximum motion displacement" plots helping them to better assess wall motion and LV Ejection-Fraction (LVEF). The outputs of the method can further help echo-technicians to assess and improve the quality of the echocardiogram recording. A major contribution of this study is the first public echo database collection composed by physicians at the Hamad Medical Corporation Hospital in Qatar. The so-called HMC-QU database will serve as the benchmark for the forthcoming relevant studies. The results over the HMC-QU dataset show that the proposed approach can achieve high accuracy, sensitivity and precision in MI detection even though the echo quality is quite poor, and the temporal resolution is low.
Automatic segmentation of myocardial contours and relevant areas like infraction and no-reflow is an important step for the quantitative evaluation of myocardial infarction. In this work, we propose a cascaded convolutional neural network for automatic myocardial infarction segmentation from delayed-enhancement cardiac MRI. We first use a 2D U-Net to focus on the intra-slice information to perform a preliminary segmentation. After that, we use a 3D U-Net to utilize the volumetric spatial information for a subtle segmentation. Our method is evaluated on the MICCAI 2020 EMIDEC challenge dataset …
ImViA Laboratory, University of Burgundy（勃艮第大学ImViA实验室）；CHU Dijon（第戎大学医院）；CGFL；Institut Curie（居里研究所）
In this paper, we propose a new deep learning framework for an automatic myocardial infarction evaluation from clinical information and delayed enhancement-MRI (DE-MRI). The proposed framework addresses two tasks. The first task is automatic detection of myocardial contours, the infarcted area, the no-reflow area, and the left ventricular cavity from a short-axis DE-MRI series. It employs two segmentation neural networks. The first network is used to segment the anatomical structures such as the myocardium and left ventricular cavity. The second network is used to segment the pathological area…
Nanjing University of Science and Technology（南京理工大学）
Automatic evaluation of myocardium and pathology plays an important role in the quantitative analysis of patients suffering from myocardial infarction. In this paper, we present a cascaded convolutional neural network framework for myocardial infarction segmentation and classification in delayed-enhancement cardiac MRI. Specifically, we first use a 2D U-Net to segment the whole heart, including the left ventricle and the myocardium. Then, we crop the whole heart as a region of interest (ROI). Finally, a new 2D U-Net is used to segment the infraction and no-reflow areas in the whole heart ROI. …
In this report, I investigate the use of end-to-end deep residual learning with dilated convolutions for myocardial infarction (MI) detection and localization from electrocardiogram (ECG) signals. Although deep residual learning has already been applied to MI detection and localization, I propose a more accurate system that distinguishes among a higher number (i.e., six) of MI locations. Inspired by speech waveform processing with neural networks, I found a more robust front-end than directly arranging the multi-lead ECG signal into an input matrix consisting of the use of a single one-dimensi…
This work evaluates deep learning-based myocardial infarction (MI) quantification using Segment cardiovascular magnetic resonance (CMR) software. Segment CMR software incorporates the expectation-maximization, weighted intensity, a priori information (EWA) algorithm used to generate the infarct scar volume, infarct scar percentage, and microvascular obstruction percentage. Here, Segment CMR software segmentation algorithm is updated with semantic segmentation with U-net to achieve and evaluate fully automated or deep learning-based MI quantification. The direct observation of graphs and the nu…
Tampere University（坦佩雷大学）；Qatar University（卡塔尔大学）；Hamad Medical Corporation（哈马德医疗公司）
Myocardial infarction (MI), or commonly known as heart attack, is a life-threatening health problem worldwide from which 32.4 million people suffer each year. Early diagnosis and treatment of MI are crucial to prevent further heart tissue damages or death. The earliest and most reliable sign of ischemia is regional wall motion abnormality (RWMA) of the affected part of the ventricular muscle. Echocardiography can easily, inexpensively, and non-invasively exhibit the RWMA. In this article, we introduce a three-phase approach for early MI detection in low-quality echocardiography: 1) segmentatio…
A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images
Zhengzhou University of Light Industry（郑州轻工业大学）；Michigan Technological University（密歇根理工大学）；Xi'an University of Posts and Telecommunications（西安邮电大学）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）
The University of Winnipeg（温尼伯大学）；Qatar University（卡塔尔大学）；Hamad Medical Corporation（哈马德医疗公司）
Cardiac imaging known as echocardiography is a non-invasive tool utilized to produce data including images and videos, which cardiologists use to diagnose cardiac abnormalities in general and myocardial infarction (MI) in particular. Echocardiography machines can deliver abundant amounts of data that need to be quickly analyzed by cardiologists to help them make a diagnosis and treat cardiac conditions. However, the acquired data quality varies depending on the acquisition conditions and the patient's responsiveness to the setup instructions. These constraints are challenging to doctors especi…
University of Leicester（莱斯特大学）；NIHR Leicester Biomedical Research Centre（NIHR莱斯特生物医学研究中心）；Glenfield Hospital（格伦菲尔德医院）；Norwegian University of Science and Technology (NTNU)（挪威科技大学）；Saint-Petersburg State Electrotechnical University（圣彼得堡国立电工大学）
A "heart attack" or myocardial infarction (MI), occurs when an artery supplying blood to the heart is abruptly occluded. The "gold standard" method for imaging MI is Cardiovascular Magnetic Resonance Imaging (MRI), with intravenously administered gadolinium-based contrast (late gadolinium enhancement). However, no "gold standard" fully automated method for the quantification of MI exists. In this work, we propose an end-to-end fully automatic system (MyI-Net) for the detection and quantification of MI in MRI images. This has the potential to reduce the uncertainty due to the technical variabil…
National University of Singapore（新加坡国立大学）；National University Heart Centre（新加坡国立大学心脏中心）；National University Hospital（新加坡国立大学医院）
Automatic segmentation of the left ventricle (LV) in late gadolinium enhanced (LGE) cardiac MR (CMR) images is difficult due to the intensity heterogeneity arising from accumulation of contrast agent in infarcted myocardium. In this paper, we present a comprehensive framework for automatic 3D segmentation of the LV in LGE CMR images. Given myocardial contours in cine images as a priori knowledge, the framework initially propagates the a priori segmentation from cine to LGE images via 2D translational registration. Two meshes representing respectively endocardial and epicardial surfaces are the…
Predicting Lethal Outcome (Cause) And Understanding Key Biomarkers Linked With Acute Myocardial Infarction Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies
University of Burgundy（勃艮第大学）；University Hospital of Dijon（第戎大学医院）；University of Franche-Comté（弗朗什-孔泰大学）；CASIS Company（CASIS公司）；University of Sousse（苏塞大学）；University of Monastir（莫纳斯提尔大学）；Erasmus MC（伊拉斯姆斯大学医学中心）
A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization therapy. Delayed enhancement-MRI or DE-MRI, which is performed several minutes after injection of the contrast agent, provides high contrast between viable and nonviable myocardium and is therefore a method of choice to evaluate the extent of MI. To automatically assess myocardial status, the results of the EMIDEC challenge that focused on this task are presented in this paper. The challenge's main objectives were twofo…
FedDA-TSformer: Federated Domain Adaptation with Vision TimeSformer for Left Ventricle Segmentation on Gated Myocardial Perfusion SPECT Image
Kennesaw State University（肯尼索州立大学）；Michigan Technological University（密歇根理工大学）；Xiangya Hospital, Central South University（中南大学湘雅医院）；Chang Bing Show Chwan Memorial Hospital（彰滨秀传纪念医院）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）；Health Research Institute, Michigan Technological University（密歇根理工大学健康研究所）
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）
Myocardium segmentation of late gadolinium enhancement (LGE) Cardiac MR images is important for evaluation of infarction regions in clinical practice. The pathological myocardium in LGE images presents distinctive brightness and textures compared with the healthy tissues, making it much more challenging to be segment. Instead, the balanced-Steady State Free Precession (bSSFP) cine images show clearly boundaries and can be easily segmented. Given this fact, we propose a novel shape-transfer GAN for LGE images, which can 1) learn to generate realistic LGE images from bSSFP with the anatomical sh…
CineMyoPS: Segmenting Myocardial Pathologies from Cine Cardiac MR
School of Medical Imaging, Fujian Medical University（福建医科大学医学影像学院）；College of Physics and Information Engineering, Fuzhou University（福州大学物理与信息工程学院）；Department of Biomedical Engineering, National University of Singapore（新加坡国立大学生物医学工程系）；School of Data Science, Fudan University（复旦大学数据科学学院）；Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院仁济医院放射科）
University of Virginia（弗吉尼亚大学）；University of Virginia Health System（弗吉尼亚大学卫生系统）
Automated identification of myocardial scar from late gadolinium enhancement cardiac magnetic resonance images (LGE-CMR) is limited by image noise and artifacts such as those related to motion and partial volume effect. This paper presents a novel joint deep learning (JDL) framework that improves such tasks by utilizing simultaneously learned myocardium segmentations to eliminate negative effects from non-region-of-interest areas. In contrast to previous approaches treating scar detection and myocardium segmentation as separate or parallel tasks, our proposed method introduces a message passin…
Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI with Explicit Cardiac Motion Modeling
Department of Biomedical Engineering, National University of Singapore, Singapore（新加坡国立大学生物医学工程系）；Department of Computer Science, National University of Singapore, Singapore（新加坡国立大学计算机科学系）；Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand（奥克兰大学生物工程研究所）；School of Biomedical Engineering and Imaging Sciences, King’s College London, UK（伦敦国王学院生物医学工程与成像科学学校）；Department of Medicine, National University of Singapore, Singapore（新加坡国立大学医学系）；Department of Cardiology, National University Heart Centre Singapore, Singapore（新加坡国立心脏中心心血管科）
Imperial College London（帝国理工学院）；Technical University Munich（慕尼黑工业大学）；University of Edinburgh（爱丁堡大学）；MRC London Institute of Medical Sciences（MRC伦敦医学科学研究所）
Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR) imaging enables the assessment of regional myocardial function and is important for understanding and analyzing cardiovascular disease. However, 3D cardiac motion estimation is challenging because the acquired cine CMR images are usually 2D slices which limit the accurate estimation of through-plane motion. To address this problem, we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion fi…
National Heart and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）
Temporal patterns of cardiac motion provide important information for cardiac disease diagnosis. This pattern could be obtained by three-directional CINE multi-slice left ventricular myocardial velocity mapping (3Dir MVM), which is a cardiac MR technique providing magnitude and phase information of the myocardial motion simultaneously. However, long acquisition time limits the usage of this technique by causing breathing artifacts, while shortening the time causes low temporal resolution and may provide an inaccurate assessment of cardiac motion. In this study, we proposed a frame synthesis al…
The Hamlyn Centre for Robotic Surgery, Imperial College London（帝国理工学院哈姆林机器人外科中心）；Department of Computing, Imperial College London（帝国理工学院计算机系）；Cardiovascular Biomedical Research Unit, Royal Brompton Hospital（皇家布朗普顿医院心血管生物医学研究中心）
Purpose: Advancements in MRI Tissue Phase Velocity Mapping (TPM) allow for the acquisition of higher quality velocity cardiac images providing better assessment of regional myocardial deformation for accurate disease diagnosis, pre-operative planning and post-operative patient surveillance. Translation of TPM velocities from the scanner's reference coordinate system to the regional cardiac coordinate system requires decoupling of translational motion and motion due to myocardial deformation. Despite existing techniques for respiratory motion compensation in TPM, there is still a remaining tran…
Coronary artery disease (CAD) remains the world's leading cause of mortality and the disease burden is continually expanding as the population ages. Recently, the MR-INFORM randomised trial has demonstrated that the management of patients with stable CAD can be guided by stress perfusion cardiovascular magnetic resonance (CMR) imaging and it is non-inferior to the using the invasive reference standard of fractional flow reserve. The benefits of using stress perfusion CMR include that it is non-invasive and significantly reduces the number of unnecessary coronary revascularisations. As compared…
IMC-PINN-FE: A Physics-Informed Neural Network for Patient-Specific Left Ventricular Finite Element Modeling with Image Motion Consistency and Biomechanical Parameter Estimation
Department of Bioengineering, Imperial College London（帝国理工学院生物工程系）
