The segmentation of the left ventricle (LV) from CINE MRI images is essential to infer important clinical parameters. Typically, machine learning algorithms for automated LV segmentation use annotated contours from only two cardiac phases, diastole, and systole. In this work, we present an analysis work-flow for fully-automated LV segmentation that learns from images acquired through the cardiac cycle. The workflow consists of three components: first, for each image in the sequence, we perform an automated localization and subsequent cropping of the bounding box containing the cardiac silhouette. Second, we identify the LV contours using a Temporal Fully Convolutional Neural Network (T-FCNN), which extends Fully Convolutional Neural Networks (FCNN) through a recurrent mechanism enforcing temporal coherence across consecutive frames. Finally, we further defined the boundaries using either one of two components: fully-connected Conditional Random Fields (CRFs) with Gaussian edge potentials and Semantic Flow. Our initial experiments suggest that significant improvement in performance can potentially be achieved by using a recurrent neural network component that explicitly learns cardiac motion patterns whilst performing LV segmentation.
Indian Institute of Technology Madras（印度理工学院马德拉斯分校）
Deep fully convolutional neural network (FCN) based architectures have shown great potential in medical image segmentation. However, such architectures usually have millions of parameters and inadequate number of training samples leading to over-fitting and poor generalization. In this paper, we present a novel highly parameter and memory efficient FCN based architecture for medical image analysis. We propose a novel up-sampling path which incorporates long skip and short-cut connections to overcome the feature map explosion in FCN like architectures. In order to processes the input images at …
Automated segmentation of human cardiac magnetic resonance datasets has been steadily improving during recent years. However, these methods are not directly applicable in preclinical context due to limited datasets and lower image resolution. Successful application of deep architectures for rat cardiac segmentation, although of critical importance for preclinical evaluation of cardiac function, has to our knowledge not yet been reported. We developed segmentation models that expand on the standard U-Net architecture and evaluated separate models for systole and diastole phases, 2MSA, and one m…
Institute of Computing, Universidade Federal Fluminense (UFF)（弗鲁米嫩塞联邦大学计算研究所）；School of Medicine, Universidade Federal do Rio de Janeiro (UFRJ)（里约热内卢联邦大学医学院）
The quantification of fat depots on the surroundings of the heart is an accurate procedure for evaluating health risk factors correlated with several diseases. However, this type of evaluation is not widely employed in clinical practice due to the required human workload. This work proposes a novel technique for the automatic segmentation of cardiac fat pads. The technique is based on applying classification algorithms to the segmentation of cardiac CT images. Furthermore, we extensively evaluate the performance of several algorithms on this task and discuss which provided better predictive mo…
Cardiovascular Research Institute, University of California, San Francisco（加州大学旧金山分校心血管研究所）；Department of Electrical Engineering and Computer Science, University of California at Berkeley（加州大学伯克利分校电气工程与计算机科学系）；Department of Medicine, University of California, San Francisco（加州大学旧金山分校医学系）；Division of Cardiology, Department of Medicine, and Feinberg Cardiovascular Research Institute（医学系心脏病学科及范伯格心血管研究所）
Automated cardiac image interpretation has the potential to transform clinical practice in multiple ways including enabling low-cost serial assessment of cardiac function in the primary care and rural setting. We hypothesized that advances in computer vision could enable building a fully automated, scalable analysis pipeline for echocardiogram (echo) interpretation. Our approach entailed: 1) preprocessing; 2) convolutional neural networks (CNN) for view identification, image segmentation, and phasing of the cardiac cycle; 3) quantification of chamber volumes and left ventricular mass; 4) parti…
Automated cardiac segmentation from magnetic resonance imaging datasets is an essential step in the timely diagnosis and management of cardiac pathologies. We propose to tackle the problem of automated left and right ventricle segmentation through the application of a deep fully convolutional neural network architecture. Our model is efficiently trained end-to-end in a single learning stage from whole-image inputs and ground truths to make inference at every pixel. To our knowledge, this is the first application of a fully convolutional neural network architecture for pixel-wise labeling in ca…
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…
Imperial College London（帝国理工学院）；MRC London Institute of Medical Sciences（MRC伦敦医学科学研究所）
In this paper we introduce a novel and accurate optimisation method for segmentation of cardiac MR (CMR) images in patients with pulmonary hypertension (PH). The proposed method explicitly takes into account the image features learned from a deep neural network. To this end, we estimate simultaneous probability maps over region and edge locations in CMR images using a fully convolutional network. Due to the distinct morphology of the heart in patients with PH, these probability maps can then be incorporated in a single nested level set optimisation framework to achieve multi-region segmentatio…
Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）
Segmentation of cardiac fibrosis and scar are essential for clinical diagnosis and can provide invaluable guidance for the treatment of cardiac diseases. Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been successful for its efficacy in guiding the clinical diagnosis and treatment reliably. For LGE CMR, many methods have demonstrated success in accurately segmenting scarring regions. Co-registration with other non-contrast-agent (non-CA) modalities, balanced steady-state free precession (bSSFP) and cine magnetic resonance imaging (MRI) for example, can further en…
Robust automated calcification meshing for biomechanical cardiac digital twins
Yale University（耶鲁大学）；Texas Tech University（德克萨斯理工大学）；Massachusetts Institute of Technology（麻省理工学院）；Hartford Hospital（哈特福德医院）；Georgia Institute of Technology（佐治亚理工学院）
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）
Myocardial characterization is essential for patients with myocardial infarction and other myocardial diseases, and the assessment is often performed using cardiac magnetic resonance (CMR) sequences. In this study, we propose a fully automated approach using deep convolutional neural networks (CNN) for cardiac pathology segmentation, including left ventricular (LV) blood pool, right ventricular blood pool, LV normal myocardium, LV myocardial edema (ME) and LV myocardial scars (MS). The input to the network consists of three CMR sequences, namely, late gadolinium enhancement (LGE), T2 and balan…
National Heart & Lung Institute, Imperial College London（伦敦帝国理工学院国家心肺研究所）；Royal Brompton Hospital（皇家布朗普顿医院）
Four-dimensional (4D) left ventricular myocardial velocity mapping (MVM) is a cardiac magnetic resonance (CMR) technique that allows assessment of cardiac motion in three orthogonal directions. Accurate and reproducible delineation of the myocardium is crucial for accurate analysis of peak systolic and diastolic myocardial velocities. In addition to the conventionally available magnitude CMR data, 4D MVM also acquires three velocity-encoded phase datasets which are used to generate velocity maps. These can be used to facilitate and improve myocardial delineation. Based on the success of deep l…
Austin Health（奥斯汀健康）；The University of Melbourne（墨尔本大学）；Kings College London（伦敦国王学院）；I-MED Radiology（I-MED放射科）；University of Porto（波尔图大学）
Objectives: To develop an image-based automatic deep learning method to classify cardiac MR images by sequence type and imaging plane for improved clinical post-processing efficiency. Methods: Multi-vendor cardiac MRI studies were retrospectively collected from 4 centres and 3 vendors. A two-head convolutional neural network ('CardiSort') was trained to classify 35 sequences by imaging sequence (n=17) and plane (n=10). Single vendor training (SVT) on single centre images (n=234 patients) and multi-vendor training (MVT) with multicentre images (n = 479 patients, 3 centres) was performed. Model …
Imperial College London（伦敦帝国学院）；MRC London Institute of Medical Sciences（MRC伦敦医学科学研究所）
In the clinical routine, short axis (SA) cine cardiac MR (CMR) image stacks are acquired during multiple subsequent breath-holds. If the patient cannot consistently hold the breath at the same position, the acquired image stack will be affected by inter-slice respiratory motion and will not correctly represent the cardiac volume, introducing potential errors in the following analyses and visualisations. We propose an approach to automatically correct inter-slice respiratory motion in SA CMR image stacks. Our approach makes use of probabilistic segmentation maps (PSMs) of the left ventricular (…
BAAI Cardiac Agent: An intelligent multimodal agent for automated reasoning and diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging
Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）；Department of Radiology, Beijing Anzhen Hospital, Beijing Institute of Heart, Lung & Vascular Diseases, Capital Medical University（首都医科大学附属北京安贞医院放射科，北京市心肺血管疾病研究所）；Department of MR, the First Affiliated Hospital, Henan Medical University（河南医科大学第一附属医院磁共振科）；Department of Cardiology, Clinical Center for Coronary Heart Disease, Beijing Institute of Heart, Lung and Blood Vessel Disease, Beijing Anzhen Hospital, Capital Medical University（首都医科大学附属北京安贞医院心内科，冠心病临床中心，北京市心肺血管疾病研究所）；Department of Cardiac Surgery, Beijing Anzhen Hospital, Institute of Heart, Lung and Vascular Diseases, Capital Medical University（首都医科大学附属北京安贞医院心脏外科，心肺血管疾病研究所）
Many strides have been made in semantic segmentation of multiple classes within an image. This has been largely due to advancements in deep learning and convolutional neural networks (CNNs). Features within a CNN are automatically learned during training, which allows for the abstraction of semantic information within the images. These deep learning models are powerful enough to handle the segmentation of multiple classes without the need for multiple networks. Despite these advancements, few attempts have been made to automatically segment multiple anatomical features within medical imaging d…
From Raw Segmentations to Simulation-Ready Cardiac Meshes: An Automated Framework for Anatomical Reconstruction and Virtual Cohort Generation
Gran Sasso Science Institute (GSSI)（大萨索科学研究所）；European Hospital（欧洲医院）；University of Edinburgh（爱丁堡大学）；Physics of Fluids Group, University of Twente（特温特大学流体物理小组）；INFN–Laboratori Nazionali del Gran Sasso（意大利国家核物理研究所大萨索国家实验室）
Automated Interpretable 2D Video Extraction from 3D Echocardiography
University of California, Los Angeles（加州大学洛杉矶分校）；Kaiser Permanente Division of Research（凯撒医疗集团研究部）；Cedars-Sinai Medical Center（西达赛西医学中心）；Stanford University（斯坦福大学）
Guy’s and St Thomas’ Hospital NHS Foundation Trust（盖伊和圣托马斯医院NHS基金会信托）；King’s College London（伦敦国王学院）；Imperial College London（帝国理工学院）
Quality assessment of medical images is essential for complete automation of image processing pipelines. For large population studies such as the UK Biobank, artefacts such as those caused by heart motion are problematic and manual identification is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) images. As this is a highly imbalanced classification problem (due to the high number of good qualit…
Universidade Federal Fluminense (UFF)（弗鲁米嫩塞联邦大学）；Universidade Federal do Rio de Janeiro (UFRJ)（里约热内卢联邦大学）；Universidade Federal do Espírito Santo (UFES)（圣埃斯皮里图联邦大学）
The deposits of fat on the surroundings of the heart are correlated to several health risk factors such as atherosclerosis, carotid stiffness, coronary artery calcification, atrial fibrillation and many others. These deposits vary unrelated to obesity, which reinforces its direct segmentation for further quantification. However, manual segmentation of these fats has not been widely deployed in clinical practice due to the required human workload and consequential high cost of physicians and technicians. In this work, we propose a unified method for an autonomous segmentation and quantification…
Friedrich-Alexander-Universität 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）
Left ventricle segmentation and morphological assessment are essential for improving diagnosis and our understanding of cardiomyopathy, which in turn is imperative for reducing risk of myocardial infarctions in patients. Convolutional neural network (CNN) based methods for cardiac magnetic resonance (CMR) image segmentation rely on supervision with pixel-level annotations, and may not generalize well to images from a different domain. These methods are typically sensitive to variations in imaging protocols and data acquisition. Since annotating multi-sequence CMR images is tedious and subject …
Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation. However, cine CMR acquisition is inherently slow and in recent decades considerable effort has been put into accelerating scan times without compromising image quality or the accuracy of derived results. In this paper, we present a fully-automated, quality-controlled integrated framework for reconstruction, segmentation and downstream analysis of undersampled cine CMR data. The framework enables active acquisition of radial k-space data, in which acquisition can be stopped as soon as …
Eindhoven University of Technology（埃因霍温理工大学）；Philips Healthcare（飞利浦医疗保健）；King’s College London（伦敦国王学院）
Background: The clinical utility of late gadolinium enhancement (LGE) cardiac MRI is limited by the lack of standardization, and time-consuming postprocessing. In this work, we tested the hypothesis that a cascaded deep learning pipeline trained with augmentation by synthetically generated data would improve model accuracy and robustness for automated scar quantification. Methods: A cascaded pipeline consisting of three consecutive neural networks is proposed, starting with a bounding box regression network to identify a region of interest around the left ventricular (LV) myocardium. Two furth…
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…
Automated Cardiac Resting Phase Detection Targeted on the Right Coronary Artery
Friedrich-Alexander University Erlangen-Nuremberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Siemens Healthcare GmbH（西门子医疗有限公司）；Siemens Medical Solutions USA, Inc.（美国西门子医疗解决方案公司）；Jose Michel Kalaf Research Institute（何塞·米歇尔·卡拉夫研究所）；Diagnostikum Berlin（柏林诊断中心）
In this paper, we develop a 2D and 3D segmentation pipelines for fully automated cardiac MR image segmentation using Deep Convolutional Neural Networks (CNN). Our models are trained end-to-end from scratch using the ACD Challenge 2017 dataset comprising of 100 studies, each containing Cardiac MR images in End Diastole and End Systole phase. We show that both our segmentation models achieve near state-of-the-art performance scores in terms of distance metrics and have convincing accuracy in terms of clinical parameters. A comparative analysis is provided by introducing a novel dice loss functio…
Imperial College London（伦敦帝国学院）；King’s College London（伦敦国王学院）；St Thomas’ Hospital（圣托马斯医院）
Prenatal screening with ultrasound can lower neonatal mortality significantly for selected cardiac abnormalities. However, the need for human expertise, coupled with the high volume of screening cases, limits the practically achievable detection rates. In this paper we discuss the potential for deep learning techniques to aid in the detection of congenital heart disease (CHD) in fetal ultrasound. We propose a pipeline for automated data curation and classification. During both training and inference, we exploit an auxiliary view classification task to bias features toward relevant cardiac stru…
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…
4D Flow is an MRI sequence which allows acquisition of 3D images of the heart. The data is typically acquired volumetrically, so it must be reformatted to generate cardiac long axis and short axis views for diagnostic interpretation. These views may be generated by placing 6 landmarks: the left and right ventricle apex, and the aortic, mitral, pulmonary, and tricuspid valves. In this paper, we propose an automatic method to localize landmarks in order to compute the cardiac views. Our approach consists of first calculating a bounding box that tightly crops the heart, followed by a landmark loc…
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…
NVIDIA（英伟达）；Lenovo Research（联想研究院）；Piedmont Heart Institute（皮埃蒙特心脏研究所）；Tencent Medical AI Lab（腾讯医疗人工智能实验室）；University of California, Irvine（加州大学欧文分校）
Echocardiography has become routinely used in the diagnosis of cardiomyopathy and abnormal cardiac blood flow. However, manually measuring myocardial motion and cardiac blood flow from echocardiogram is time-consuming and error-prone. Computer algorithms that can automatically track and quantify myocardial motion and cardiac blood flow are highly sought after, but have not been very successful due to noise and high variability of echocardiography. In this work, we propose a neural multi-scale self-supervised registration (NMSR) method for automated myocardial and cardiac blood flow dense track…
Siemens SRL（西门子SRL公司）；Transilvania University of Brasov（布拉索夫特兰西瓦尼亚大学）；Clinical Emergency Hospital（临床急诊医院）；Siemens Healthcare GmbH（西门子医疗保健有限公司）；Siemens Healthineers（西门子医疗）
Invasive coronary angiography (ICA) is the gold standard in Coronary Artery Disease (CAD) imaging. Detection of the end-diastolic frame (EDF) and, in general, cardiac phase detection on each temporal frame of a coronary angiography acquisition is of significant importance for the anatomical and non-invasive functional assessment of CAD. This task is generally performed via manual frame selection or semi-automated selection based on simultaneously acquired ECG signals - thus introducing the requirement of simultaneous ECG recordings. We evaluate the performance of a purely image based workflow …
Broadly speaking, the objective in cardiac image segmentation is to delineate the outer and inner walls of the heart to segment out either the entire or parts of the organ boundaries. This paper will focus on MR images as they are the most widely used in cardiac segmentation -- as a result of the accurate morphological information and better soft tissue contrast they provide. This cardiac segmentation information is very useful as it eases physical measurements that provides useful metrics for cardiac diagnosis such as infracted volumes, ventricular volumes, ejection fraction, myocardial mass,…
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…
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…
Automatic diagnosis of cardiac magnetic resonance images based on semi-supervised learning
School of Information and Electrical Engineering, Hunan University of Science and Technology（湖南科技大学信息与电气工程学院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Guilin Medical University（桂林医学院）；Rucheng County Hospital of Traditional Chinese Medicine（汝城县中医医院）
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images
The accurate quantification of left ventricular (LV) deformation/strain shows significant promise for quantitatively assessing cardiac function for use in diagnosis and therapy planning (Jasaityte et al., 2013). However, accurate estimation of the displacement of myocardial tissue and hence LV strain has been challenging due to a variety of issues, including those related to deriving tracking tokens from images and following tissue locations over the entire cardiac cycle. In this work, we propose a point matching scheme where correspondences are modeled as flow through a graphical network. Myo…
Robust Deep Learning for Myocardial Scar Segmentation in Cardiac MRI with Noisy Labels
Department of Cardiovascular Sciences, University of Leicester, Leicester, UK（莱斯特大学心血管科学系）；NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK（莱斯特生物医学研究中心）；Department of Information Engineering and Mathematics, University of Siena, Italy（锡耶纳大学信息工程与数学系）；Department of Mathematics, University of Leicester, Leicester, UK（莱斯特大学数学系）；Department of Computer Science, University of Copenhagen, Copenhagen, DK（哥本哈根大学计算机科学系）
Biomedical Image Analysis Group, Department of Computing, Imperial College London（帝国理工学院计算机系生物医学图像分析组）；Division of Brain Sciences, Dept. of Medicine, Imperial College London（帝国理工学院医学部脑科学系）；GlaxoSmithKline Research and Development（葛兰素史克研发部）；William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London（伦敦玛丽女王大学威廉·哈维研究所（NIHR巴茨生物医学研究中心））；Barts Heart Centre, Barts Health NHS Trust（巴茨健康国民医疗服务信托基金巴茨心脏中心）；Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford（牛津大学拉德克利夫医学部心血管医学系）；Department of Radiology, Severance Hospital, Yonsei University College of Medicine（延世大学医学院塞弗伦斯医院放射科）；UK Dementia Research Institute, Imperial College London（帝国理工学院英国痴呆症研究所）
Background: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools, e.g. image segmentation methods, are employed to derive quantitative measures or biomarkers for later analyses. Manual inspection and visual QC of each segmentation isn't feasible at large scale. However, it's important to be able to automatically detect when a segmentation method fails so as to avoid inclusion of wrong measurements into subsequent analyses which could lead to incorrect conclusions. Met…
Information Technology University (ITU)-Punjab（旁遮普信息技术大学）；COMSATS Institute of Information Technology, Islamabad（伊斯兰堡COMSATS信息技术学院）；University of Southern Queensland（南昆士兰大学）
Cardiac auscultation involves expert interpretation of abnormalities in heart sounds using stethoscope. Deep learning based cardiac auscultation is of significant interest to the healthcare community as it can help reducing the burden of manual auscultation with automated detection of abnormal heartbeats. However, the problem of automatic cardiac auscultation is complicated due to the requirement of reliability and high accuracy, and due to the presence of background noise in the heartbeat sound. In this work, we propose a Recurrent Neural Networks (RNNs) based automated cardiac auscultation s…
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心影像科学研究所）；Department of Radiology, University Medical Center Utrecht（乌得勒支大学医学中心放射科）
Segmentation of the heart in cardiac cine MR is clinically used to quantify cardiac function. We propose a fully automatic method for segmentation and disease classification using cardiac cine MR images. A convolutional neural network (CNN) was designed to simultaneously segment the left ventricle (LV), right ventricle (RV) and myocardium in end-diastole (ED) and end-systole (ES) images. Features derived from the obtained segmentations were used in a Random Forest classifier to label patients as suffering from dilated cardiomyopathy, hypertrophic cardiomyopathy, heart failure following myocard…
In recent years, convolutional neural networks have demonstrated promising performance in a variety of medical image segmentation tasks. However, when a trained segmentation model is deployed into the real clinical world, the model may not perform optimally. A major challenge is the potential poor-quality segmentations generated due to degraded image quality or domain shift issues. There is a timely need to develop an automated quality control method that can detect poor segmentations and feedback to clinicians. Here we propose a novel deep generative model-based framework for quality control …
Western University（西安大略大学）；Digital Imaging Group of London（伦敦数字影像集团）
Cardiac indices estimation is of great importance during identification and diagnosis of cardiac disease in clinical routine. However, estimation of multitype cardiac indices with consistently reliable and high accuracy is still a great challenge due to the high variability of cardiac structures and complexity of temporal dynamics in cardiac MR sequences. While efforts have been devoted into cardiac volumes estimation through feature engineering followed by a independent regression model, these methods suffer from the vulnerable feature representation and incompatible regression model. In this…
Cardiac Magnetic Resonance (CMR) imaging is commonly used to assess cardiac structure and function. One disadvantage of CMR is that post-processing of exams is tedious. Without automation, precise assessment of cardiac function via CMR typically requires an annotator to spend tens of minutes per case manually contouring ventricular structures. Automatic contouring can lower the required time per patient by generating contour suggestions that can be lightly modified by the annotator. Fully convolutional networks (FCNs), a variant of convolutional neural networks, have been used to rapidly advan…
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属协和医院放射科）；Hubei Province Key Laboratory of Molecular Imaging（湖北省分子影像重点实验室）；HUST-HW Joint Innovation Lab（华中科技大学-华为联合创新实验室）
Cardiac MRI segmentation plays a crucial role in clinical diagnosis for evaluating personalized cardiac performance parameters. Due to the indistinct boundaries and heterogeneous intensity distributions in the cardiac MRI, most existing methods still suffer from two aspects of challenges: inter-class indistinction and intra-class inconsistency. To tackle these two problems, we propose a novel method to exploit the directional feature maps, which can simultaneously strengthen the differences between classes and the similarities within classes. Specifically, we perform cardiac segmentation and l…
In the United States, heart disease is the leading cause of death for both men and women, accounting for 610,000 deaths each year [1]. Physicians use Magnetic Resonance Imaging (MRI) scans to take images of the heart in order to non-invasively estimate its structural and functional parameters for cardiovascular diagnosis and disease management. The end-systolic volume (ESV) and end-diastolic volume (EDV) of the left ventricle (LV), and the ejection fraction (EF) are indicators of heart disease. These measures can be derived from the segmented contours of the LV; thus, consistent and accurate s…
Accurate segmentation of the heart is an important step towards evaluating cardiac function. In this paper, we present a fully automated framework for segmentation of the left (LV) and right (RV) ventricular cavities and the myocardium (Myo) on short-axis cardiac MR images. We investigate various 2D and 3D convolutional neural network architectures for this task. We investigate the suitability of various state-of-the art 2D and 3D convolutional neural network architectures, as well as slight modifications thereof, for this task. Experiments were performed on the ACDC 2017 challenge training da…
