Deep Learning Segmentation in 2D echocardiography using the CAMUS dataset : Automatic Assessment of the Anatomical Shape Validity
Sarah Leclerc, Erik Smistad, Andreas Østvik, Frederic Cervenansky, Florian Espinosa, Torvald Espeland, Erik Andreas Rye Berg, Pierre-Marc Jodoin, Thomas Grenier… 展开作者
Sarah Leclerc, Erik Smistad, Andreas Østvik, Frederic Cervenansky, Florian Espinosa, Torvald Espeland, Erik Andreas Rye Berg, Pierre-Marc Jodoin, Thomas Grenier, Carole Lartizien, Lasse Lovstakken, Olivier Bernard
We recently published a deep learning study on the potential of encoder-decoder networks for the segmentation of the 2D CAMUS ultrasound dataset. We propose in this abstract an extension of the evaluation criteria to anatomical assessment, as traditional geometric and clinical metrics in cardiac segmentation do not take into account the anatomical correctness of the predicted shapes. The completed study sheds a new light on the ranking of models.
Compute-Optimal Network Design for Echocardiography Myocardial Segmentation and Perfusion Quantification using Neural Scaling Laws
Department of Bioengineering, Imperial College London（生物工程系，帝国理工学院伦敦分校）；National Heart and Lung Institute, Imperial College London（国家心脏和肺 institute，帝国理工学院伦敦分校）；Guy’s and St. Thomas’ NHS Foundation Trust（圣泰莫斯国家健康服务信托基金）
Extraction of volumetric indices from echocardiography: which deep learning solution for clinical use?
Univ Lyon（里昂大学）；Université Claude Bernard Lyon 1（里昂第一大学）；UJM-Saint Etienne（圣艾蒂安让·莫内大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；University of Sherbrooke（舍布鲁克大学）；Hospices Civils de Lyon（里昂公立医院集团）
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…
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 …
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…
EDMAE: An Efficient Decoupled Masked Autoencoder for Standard View Identification in Pediatric Echocardiography
Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属上海儿童医学中心）；Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP)（上海智能儿科工程技术研究中心）；East China Normal University（华东师范大学）；University of Shanghai for Science and Technology（上海理工大学）
IBM Research – Almaden Research Center（IBM阿尔马登研究中心）；Eastern Virginia Medical School（东弗吉尼亚医学院）
Congenital heart disease (CHD) is the most common congenital abnormality associated with birth defects in the United States. Despite training efforts and substantial advancement in ultrasound technology over the past years, CHD remains an abnormality that is frequently missed during prenatal ultrasonography. Therefore, computer-aided detection of CHD can play a critical role in prenatal care by improving screening and diagnosis. Since many CHDs involve structural abnormalities, automatic segmentation of anatomical structures is an important step in the analysis of fetal echocardiograms. While …
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）
EchoJEPA: A Latent Predictive Foundation Model for Echocardiography
University Health Network（大学健康网络）；University of Toronto（多伦多大学）；University of Chicago（芝加哥大学）；University of California, San Francisco（加州大学旧金山分校）；Vector Institute（向量研究所）；Cohere Labs（Cohere实验室）
University of Sherbrooke（舍布鲁克大学）；Univ Lyon（里昂大学）；Université Claude Bernard Lyon 1（里昂第一大学）；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）；Institut Universitaire de France（法国大学研究院）
Convolutional neural networks (CNN) have demonstrated their ability to segment 2D cardiac ultrasound images. However, despite recent successes according to which the intra-observer variability on end-diastole and end-systole images has been reached, CNNs still struggle to leverage temporal information to provide accurate and temporally consistent segmentation maps across the whole cycle. Such consistency is required to accurately describe the cardiac function, a necessary step in diagnosing many cardiovascular diseases. In this paper, we propose a framework to learn the 2D+time apical long-axi…
Reinforcement Learning for Unsupervised Domain Adaptation in Spatio-Temporal Echocardiography Segmentation
Department of Computer Science, University of Sherbrooke（谢布鲁克大学计算机科学系）；INSA, Universite Claude Bernard Lyon 1, CNRS UMR 5220, Inserm U1206, CREATIS（里昂1大学INSA、CNRS UMR 5220、Inserm U1206、CREATIS）；Dep. of Software and Information Technology Engineering, École de technologie supérieure（蒙特利尔工程学院软件与信息技术工程系）；Institut Universitaire de France (IUF)（法国国家科学院（IUF））
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）
China University of Petroleum（中国石油大学）；Qingdao Women and Children’s Hospital（青岛市妇女儿童医院）
Echocardiography is a powerful prenatal examination tool for early diagnosis of fetal congenital heart diseases (CHDs). The four-chamber (FC) view is a crucial and easily accessible ultrasound (US) image among echocardiography images. Automatic analysis of FC views contributes significantly to the early diagnosis of CHDs. The first step to automatically analyze fetal FC views is locating the fetal four crucial chambers of heart in a US image. However, it is a greatly challenging task due to several key factors, such as numerous speckles in US images, the fetal cardiac chambers with small size …
Standard views in two-dimensional echocardiography are well established but the quality of acquired images are highly dependent on operator skills and are assessed subjectively. This study is aimed at providing an objective assessment pipeline for echocardiogram image quality by defining a new set of domain-specific quality indicators. Consequently, image quality assessment can thus be automated to enhance clinical measurements, interpretation, and real-time optimization. We have developed deep neural networks for the automated assessment of echocardiographic frame which were randomly sampled …
A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation
Shanghai University（上海大学）；Shanghai Institute for Advanced Communication and Data Science（上海先进通信与数据科学研究院）；Shanghai Jiao Tong University（上海交通大学）；East China Normal University（华东师范大学）
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…
DCD: A Semantic Segmentation Model for Fetal Ultrasound Four-Chamber View
School of Information and Communication（信息与通信学院）；Guilin University of Electronic Technology（桂林电子科技大学）；Faculty of Humanities and Arts（人文艺术学院）；Macau University of Science and Technology（澳门科技大学）；Guangxi Key Laboratory of Machine Vision and Intelligent Control（广西机器视觉与智能控制重点实验室）；Wuzhou University（梧州大学）；Faculty of Innovation Engineering（创新工程学院）；Department of Public Education（公共教育系）；Wuzhou Medical College（梧州医学院）；College of Big Data and Software Engineering（大数据与软件工程学院）；Shenzhen Maternity and Child Healthcare Hospital（深圳妇幼保健医院）；Southern Medical University（南方医科大学）；School of Microelectronics（微电子学院）
