A Novel Deep Learning Based Approach for Left Ventricle Segmentation in Echocardiography: MFP-Unet
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）
Shakiba Moradi, Mostafa Ghelich-Oghli, Azin Alizadehasl, Isaac Shiri, Niki Oveisi, Mehrdad Oveisi, Majid Maleki, Jan Dhooge
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 have proposed a novel architecture to tackle this drawback. Feature maps in all levels of the decoder path of U-net are concatenated, their depths are equalized, and up-sampled to a fixed dimension. This stack of feature maps would be the input of the semantic segmentation layer. The proposed network yielded state-of-the-art results when comparing with results from U-net, dilated U-net, and deeplabv3, using the same dataset. An average Dice Metric (DM) of 0.945, Hausdorff Distance (HD) of 1.62, Jaccard Coefficient (JC) of 0.97, and Mean Absolute Distance (MAD) of 1.32 are achieved. The correlation graph, bland-altman analysis, and box plot showed a great agreement between automatic and manually calculated volume, area, and length.
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 …
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…
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 rev…
Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）
Myocardial contrast echocardiography (MCE) is an imaging technique that assesses left ventricle function and myocardial perfusion for the detection of coronary artery diseases. Automatic MCE perfusion quantification is challenging and requires accurate segmentation of the myocardium from noisy and time-varying images. Random forests (RF) have been successfully applied to many medical image segmentation tasks. However, the pixel-wise RF classifier ignores contextual relationships between label outputs of individual pixels. RF which only utilizes local appearance features is also susceptible to …
University of Oslo（奥斯陆大学）；GE Vingmed Ultrasound（GE维ingmed超声公司）；GE Research（通用电气研究中心）
Accurate and consistent predictions of echocardiography parameters are important for cardiovascular diagnosis and treatment. In particular, segmentations of the left ventricle can be used to derive ventricular volume, ejection fraction (EF) and other relevant measurements. In this paper we propose a new automated method called EchoGraphs for predicting ejection fraction and segmenting the left ventricle by detecting anatomical keypoints. Models for direct coordinate regression based on Graph Convolutional Networks (GCNs) are used to detect the keypoints. GCNs can learn to represent the cardiac…
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…
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…
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）
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）
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…
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）
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…
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…
Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification
Harvard Medical School and Massachusetts General Hospital（哈佛医学院和麻省总医院）；Graduate Institute of Biomedical Electronics and Bioinformatics（生物医学电子与生物信息学研究生院）；University of Southern California（南加州大学）
