Following the successful application of the U-Net to medical images, there have been different encoder-decoder models proposed as an improvement to the original U-Net for segmenting echocardiographic images. This study aims to examine the performance of the state-of-the-art proposed models as well as the original U-Net model by applying them to segment the endocardium of the Left Ventricle in 2D automatically. The prediction outputs of the models are used to evaluate the performance of the models by comparing the automated results against the expert annotations (gold standard). Our results reveal that the U-Net model outperforms other models by achieving an average Dice coefficient of 0.92$ \pm 0.05$, and Hausdorff distance of 3.97$ \pm 0.82$.
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）
Purpose: Echocardiography is commonly used as a non-invasive imaging tool in clinical practice for the assessment of cardiac function. However, delineation of the left ventricle is challenging due to the inherent properties of ultrasound imaging, such as the presence of speckle noise and the low signal-to-noise ratio. Methods: We propose a semi-automated segmentation algorithm for the delineation of the left ventricle in temporal 3D echocardiography sequences. The method requires minimal user interaction and relies on a diffeomorphic registration approach. Advantages of the method include no d…
Ultromics Ltd（Ultromics有限公司）；King’s College London（伦敦国王学院）；Sherbrooke University（舍布鲁克大学）
We propose a new method to automatically contour the left ventricle on 2D echocardiographic images. Unlike most existing segmentation methods, which are based on predicting segmentation masks, we focus at predicting the endocardial contour and the key landmark points within this contour (basal points and apex). This provides a representation that is closer to how experts perform manual annotations and hence produce results that are physiologically more plausible. Our proposed method uses a two-headed network based on the U-Net architecture. One head predicts the 7 contour points, and the other…
Med Fanavarn Plus Co.（梅德法纳瓦尔恩普拉斯公司）；Sharif University of Technology（谢里夫理工大学）；Rajaie Cardiovascular Medical and Research Center（拉贾伊心血管医疗与研究中心）；Iran University of Medical Science（伊朗医科大学）；Geneva University Hospital（日内瓦大学医院）；The University of British Columbia（不列颠哥伦比亚大学）；KU Leuven（鲁汶大学）
Segmentation of the Left ventricle (LV) is a crucial step for quantitative measurements such as area, volume, and ejection fraction. However, the automatic LV segmentation in 2D echocardiographic images is a challenging task due to ill-defined borders, and operator dependence issues (insufficient reproducibility). U-net, which is a well-known architecture in medical image segmentation, addressed this problem through an encoder-decoder path. Despite outstanding overall performance, U-net ignores the contribution of all semantic strengths in the segmentation procedure. In the present study, we h…
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Contrastive learning has proven useful in many applications where access to labelled data is limited. The lack of annotated data is particularly problematic in medical image segmentation as it is difficult to have clinical experts manually annotate large volumes of data such as cardiac structures in ultrasound images of the heart. In this paper, We propose a self supervised contrastive learning method to segment the left ventricle from echocardiography when limited annotated images exist. Furthermore, we study the effect of contrastive pretraining on two well-known segmentation networks, UNet …
The goal of this project is to use magnetic resonance imaging (MRI) data to provide an end-to-end analytics pipeline for left and right ventricle (LV and RV) segmentation. Another aim of the project is to find a model that would be generalizable across medical imaging datasets. We utilized a variety of models, datasets, and tests to determine which one is well suited to this purpose. Specifically, we implemented three models (2-D U-Net, 3-D U-Net, and DenseNet), and evaluated them on four datasets (Automated Cardiac Diagnosis Challenge, MICCAI 2009 LV, Sunnybrook Cardiac Data, MICCAI 2012 RV).…
Manipal Institute of Technology, Manipal Academy of Higher Education（马尼帕尔高等教育学院马尼帕尔理工学院）
Segmentation of the left ventricle in cardiac magnetic resonance imaging MRI scans enables cardiologists to calculate the volume of the left ventricle and subsequently its ejection fraction. The ejection fraction is a measurement that expresses the percentage of blood leaving the heart with each contraction. Cardiologists often use ejection fraction to determine one's cardiac function. We propose multiscale template matching technique for detection and an elliptical active disc for automated segmentation of the left ventricle in MR images. The elliptical active disc optimizes the local energy …
Universitat Pompeu Fabra（庞培法布拉大学）；Normandie Univ（诺曼底大学）；UNICAEN（卡昂大学）；CHU de Caen Normandie（卡昂诺曼底大学医院）；Inserm Comete（法国国家健康与医学研究院Comete实验室）；GIP Cyceron（Cyceron集团利益公共机构）；Philips Research（飞利浦研究院）；ICREA（加泰罗尼亚高等研究院）；IDIBAPS（巴塞罗那生物医学研究所）；KULeuven（鲁汶大学）
3D echocardiography is an increasingly popular tool for assessing cardiac remodelling in the right ventricle (RV). It allows quantification of the cardiac chambers without any geometric assumptions, which is the main weakness of 2D echocardiography. However, regional quantification of geometry and function is limited by the lower spatial and temporal resolution and the scarcity of identifiable anatomical landmarks. We developed a technique for regionally assessing the 3 relevant RV regions: apical, inlet and outflow. The method's inputs are end-diastolic (ED) and end-systolic (ES) segmented 3D…
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…
Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e segmenting cardiac structures as well as estimating clinical indices, on a dataset especially designed to answer this objective. We therefore introduce the Cardiac Acquisitions for Multi-structure Ultrasound Segmentat…
GE Vingmed Ultrasound, GE Healthcare（通用电气医疗集团GE Vingmed超声部门）；University of Oslo（奥斯陆大学）；Norwegian Computing Center（挪威计算中心）
Two-dimensional echocardiography (2DE) measurements of left ventricle (LV) dimensions are highly significant markers of several cardiovascular diseases. These measurements are often used in clinical care despite suffering from large variability between observers. This variability is due to the challenging nature of accurately finding the correct temporal and spatial location of measurement endpoints in ultrasound images. These images often contain fuzzy boundaries and varying reflection patterns between frames. In this work, we present a convolutional neural network (CNN) based approach to aut…
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…
GHOST-CAT: An Efficient and Practical Network for Mesh Generation from 3D Echocardiography
Auckland Bioengineering Institute, University of Auckland（奥克兰大学生物工程研究所）；Faculty of Informatics, Telkom University（印尼电信大学信息学院）；School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与成像科学系）；Department of Engineering Science and Biomedical Engineering, University of Auckland（奥克兰大学生命科学工程系）
University of Kansas（堪萨斯大学）；Rochester Institute of Technology（罗切斯特理工学院）
Patient-specific left ventricle (LV) myocardial models have the potential to be used in a variety of clinical scenarios for improved diagnosis and treatment plans. Cine cardiac magnetic resonance (MR) imaging provides high resolution images to reconstruct patient-specific geometric models of the LV myocardium. With the advent of deep learning, accurate segmentation of cardiac chambers from cine cardiac MR images and unsupervised learning for image registration for cardiac motion estimation on a large number of image datasets is attainable. Here, we propose a deep leaning-based framework for th…
Cardiac left ventricle (LV) quantification provides a tool for diagnosing cardiac diseases. Automatic calculation of all relevant LV indices from cardiac MR images is an intricate task due to large variations among patients and deformation during the cardiac cycle. Typical methods are based on segmentation of the myocardium or direct regression from MR images. To consider cardiac motion and deformation, recurrent neural networks and spatio-temporal convolutional neural networks (CNNs) have been proposed. We study an approach combining state-of-the-art models and emphasizing transfer learning t…
The University of Hong Kong（香港大学）；Imperial College London（伦敦帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）
Myocardial Velocity Mapping Cardiac MR (MVM-CMR) can be used to measure global and regional myocardial velocities with proved reproducibility. Accurate left ventricle delineation is a prerequisite for robust and reproducible myocardial velocity estimation. Conventional manual segmentation on this dataset can be time-consuming and subjective, and an effective fully automated delineation method is highly in demand. By leveraging recently proposed deep learning-based semantic segmentation approaches, in this study, we propose a novel fully automated framework incorporating a 3D-UNet backbone arch…
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…
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…
A Data Augmentation Pipeline to Generate Synthetic Labeled Datasets of 3D Echocardiography Images using a GAN
GE Vingmed Ultrasound - GE Healthcare（GE Vingmed超声 - GE医疗）；CARIM School for Cardiovascular Diseases（CARIM心血管疾病学院）；Maastricht University Medical Center（马斯特里赫特大学医学中心）
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）
King’s College London（伦敦国王学院）；Guy’s and St Thomas’ NHS Foundation Trust（盖伊和圣托马斯国民保健服务基金会信托）；Technical University of Munich（慕尼黑工业大学）；Imperial College London（帝国理工学院）；Helmholtz Center Munich（慕尼黑亥姆霍兹中心）
Domain shift refers to the difference in the data distribution of two datasets, normally between the training set and the test set for machine learning algorithms. Domain shift is a serious problem for generalization of machine learning models and it is well-established that a domain shift between the training and test sets may cause a drastic drop in the model's performance. In medical imaging, there can be many sources of domain shift such as different scanners or scan protocols, different pathologies in the patient population, anatomical differences in the patient population (e.g. men vs wo…
Bayesian Optimization (BO) is a well-studied hyperparameter tuning technique that is more efficient than grid search for high-cost, high-parameter machine learning problems. Echocardiography is a ubiquitous modality for evaluating heart structure and function in cardiology. In this work, we use BO to optimize the architectural and training-related hyperparameters of a previously published deep fully convolutional neural network model for multi-structure segmentation in echocardiography. In a fair comparison, the resulting model outperforms this recent state-of-the-art on the annotated CAMUS da…
Phase Unwrapping of Color Doppler Echocardiography using Deep Learning
CREATIS（CREATIS实验室）；CNRS（法国国家科学研究中心）；INSERM（法国国家健康与医学研究院）；INSA Lyon（里昂国立应用科学学院）；University of Lyon 1（里昂第一大学）；Institut Universitaire de France（法兰西大学研究院）
Friedrich-Alexander-University Erlangen-Nuremberg（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）
Quantitative assessment of cardiac left ventricle (LV) morphology is essential to assess cardiac function and improve the diagnosis of different cardiovascular diseases. In current clinical practice, LV quantification depends on the measurement of myocardial shape indices, which is usually achieved by manual contouring of the endo- and epicardial. However, this process subjected to inter and intra-observer variability, and it is a time-consuming and tedious task. In this paper, we propose a spatio-temporal multi-task learning approach to obtain a complete set of measurements quantifying cardia…
This paper presents a novel method to automatically identify late-activating regions of the left ventricle from cine Displacement Encoding with Stimulated Echo (DENSE) MR images. We develop a deep learning framework that identifies late mechanical activation in heart failure patients by detecting the Time to the Onset of circumferential Shortening (TOS). In particular, we build a cascade network performing end-to-end (i) segmentation of the left ventricle to analyze cardiac function, (ii) prediction of TOS based on spatiotemporal circumferential strains computed from displacement maps, and (ii…
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…
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…
Automatic segmentation of the heart cavity is an essential task for the diagnosis of cardiac diseases. In this paper, we propose a semi-supervised segmentation setup for leveraging unlabeled data to segment Left-ventricle, Right-ventricle, and Myocardium. We utilize an enhanced version of residual U-Net architecture on a large-scale cardiac MRI dataset. Handling the class imbalanced data issue using dice loss, the enhanced supervised model is able to achieve better dice scores in comparison with a vanilla U-Net model. We applied several augmentation techniques including histogram matching to i…
Univ Lyon（里昂大学）；INSA-Lyon（里昂国立应用科学学院）；Université Claude Bernard Lyon 1（里昂第一大学克洛德·贝尔纳大学）；UJM-Saint Etienne（圣艾蒂安大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；Norwegian University of Science and Technology（挪威科技大学）；Centre Hospitalier de Saint-Etienne（圣艾蒂安中心医院）；St. Olavs Hospital（圣奥拉夫医院）；University of Sherbrooke（舍布鲁克大学）
Segmentation of cardiac structures is one of the fundamental steps to estimate volumetric indices of the heart. This step is still performed semi-automatically in clinical routine, and is thus prone to inter- and intra-observer variability. Recent studies have shown that deep learning has the potential to perform fully automatic segmentation. However, the current best solutions still suffer from a lack of robustness. In this work, we introduce an end-to-end multi-task network designed to improve the overall accuracy of cardiac segmentation while enhancing the estimation of clinical indices and…
Hospital Virgen de la Arrixaca（拉阿里克萨卡圣母医院）；University of Murcia（穆尔西亚大学）
Left ventricular non-compaction (LVNC) is a rare cardiomyopathy characterized by abnormal trabeculations in the left ventricle cavity. Although traditional computer vision approaches exist for LVNC diagnosis, deep learning-based tools could not be found in the literature. In this paper, a first approach using convolutional neural networks (CNNs) is presented. Four CNNs are trained to automatically segment the compacted and trabecular areas of the left ventricle for a population of patients diagnosed with Hypertrophic cardiomyopathy. Inference results confirm that deep learning-based approaches…
Evaluation of state-of-the-art deep learning models in the segmentation of the heart ventricles in parasternal short-axis echocardiograms
Southern Illinois University Edwardsville（南伊利诺伊大学爱德华兹维尔分校）；The Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院）；Columbus Regional Hospital（哥伦布地区医院）；St. Louis Community College（圣路易斯社区学院）
In the clinical environment, myocardial infarction (MI) as one com-mon cardiovascular disease is mainly evaluated based on the late gadolinium enhancement (LGE) cardiac magnetic resonance images (CMRIs). The auto-matic segmentations of left ventricle (LV), right ventricle (RV), and left ven-tricular myocardium (LVM) in the LGE CMRIs are desired for the aided diag-nosis in clinic. To accomplish this segmentation task, this paper proposes a modified U-net architecture by combining multi-sequence CMRIs, including the cine, LGE, and T2-weighted CMRIs. The cine and T2-weighted CMRIs are used to ass…
National Heart, Lung and Blood Institute（国家心肺血液研究所）；National Institutes of Health（美国国立卫生研究院）；Barts Heart Centre（巴特心脏中心）；Royal Free Hospital（皇家自由医院）；University of Leeds（利兹大学）
Quantification of myocardial perfusion has the potential to improve detection of regional and global flow reduction. Significant effort has been made to automate the workflow, where one essential step is the arterial input function (AIF) extraction. Since failure here invalidates quantification, high accuracy is required. For this purpose, this study presents a robust AIF detection method using the convolutional neural net (CNN) model. CNN models were trained by assembling 25,027 scans (N=12,984 patients) from three hospitals, seven scanners. A test set of 5,721 scans (N=2,805 patients) evalua…
MSV-Mamba: A Multiscale Vision Mamba Network for Echocardiography Segmentation
School of Computer and Information Engineering, Shanghai Polytechnic University（上海第二工业大学计算机与信息工程学院）；The First Affiliated Hospital of Jinan University（暨南大学附属第一医院）；Fudan University Huashan Hospital（复旦大学附属华山医院）
University of West London（西伦敦大学）；Imperial College（帝国理工学院）
Echocardiography image quality assessment is not a trivial issue in transthoracic examination. As the in vivo examination of heart structures gained prominence in cardiac diagnosis, it has been affirmed that accurate diagnosis of the left ventricle functions is hugely dependent on the quality of echo images. Up till now, visual assessment of echo images is highly subjective and requires specific definition under clinical pathologies. While poor-quality images impair quantifications and diagnosis, the inherent variations in echocardiographic image quality standards indicates the complexity face…
J.J. Strossmayer University of Osijek（奥西耶克J.J.斯特罗斯迈尔大学）
Deep learning has a great potential to alleviate diagnosis and prognosis for various clinical procedures. However, the lack of a sufficient number of medical images is the most common obstacle in conducting image-based analysis using deep learning. Due to the annotations scarcity, semi-supervised techniques in the automatic medical analysis are getting high attention. Artificial data augmentation and generation techniques such as generative adversarial networks (GANs) may help overcome this obstacle. In this work, we present an image generation approach that uses generative adversarial network…
Fudan University（复旦大学）；Imperial College London（帝国理工学院）；University of Oxford（牛津大学）；Zhongshan Hospital Affiliated to Fudan University（复旦大学附属中山医院）
The quality of cardiac magnetic resonance (CMR) imaging is susceptible to respiratory motion artifacts. The model robustness of automated segmentation techniques in face of real-world respiratory motion artifacts is unclear. This manuscript describes the design of extreme cardiac MRI analysis challenge under respiratory motion (CMRxMotion Challenge). The challenge aims to establish a public benchmark dataset to assess the effects of respiratory motion on image quality and examine the robustness of segmentation models. The challenge recruited 40 healthy volunteers to perform different breath-ho…
Crop and Couple: cardiac image segmentation using interlinked specialist networks
Queen Mary University of London（伦敦玛丽女王大学）；Queen Mary's Digital Environment Research Institute (DERI)（伦敦玛丽女王大学数字环境研究所）；University College London（伦敦大学学院）
Multi-Disease-Aware Training Strategy for Cardiac MR Image Segmentation
College of Computer Science, Sichuan Normal University（四川师范大学计算机科学学院）；School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算与人工智能学院）；West China Hospital, Sichuan University（四川大学华西医院）
Imperial College London（帝国理工学院）；University College London（伦敦大学学院）；Barts Heart Centre, St Bartholomew’s Hospital（巴茨心脏中心，圣巴塞洛缪医院）；Queen Mary University of London（伦敦玛丽女王大学）
Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR images. While CNNs can generally perform the segmentation tasks with high accuracy when training and test images come from the same domain (e.g. same scanner or site), their performance often degrades dramatically on images from different scanners or clinical sites. We propose a simple yet effective way for improving the network generalization ability by carefully designing data normalization and augmentation strateg…
Echocardiography (echo) is an indispensable tool in a cardiologist's diagnostic armamentarium. To date, almost all echocardiographic parameters require time-consuming manual labeling and measurements by an experienced echocardiographer and exhibit significant variability, owing to the noisy and artifact-laden nature of echo images. For example, mitral inflow (MI) Doppler is used to assess left ventricular (LV) diastolic function, which is of paramount clinical importance to distinguish between different cardiac diseases. In the current work we present a fully automated workflow which leverages…
