Three-Dimensional Segmentation of the Left Ventricle in Late Gadolinium Enhanced MR Images of Chronic Infarction Combining Long- and Short-Axis Information
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）
Dong Wei, Ying Sun, Sim-Heng Ong, Ping Chai, Lynette L. Teo, Adrian F. Low
备注：
Medical Image Analysis, Volume 17, Issue 6, August 2013, Pages 685-697
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 then constructed with the propagated contours. After construction, the two meshes are deformed towards the myocardial edge points detected in both short-axis and long-axis LGE images in a unified 3D coordinate system. Taking into account the intensity characteristics of the LV in LGE images, we propose a novel parametric model of the LV for consistent myocardial edge points detection regardless of pathological status of the myocardium (infarcted or healthy) and of the type of the LGE images (short-axis or long-axis). We have evaluated the proposed framework with 21 sets of real patient and 4 sets of simulated phantom data. Both distance- and region-based performance metrics confirm the observation that the framework can generate accurate and reliable results for myocardial segmentation of LGE images. We have also tested the robustness of the framework with respect to varied a priori segmentation in both practical and simulated settings. Experimental results show that the proposed framework can greatly compensate variations in the given a priori knowledge and consistently produce accurate segmentations.
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. …
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…
University of Bourgogne Franche-Comté（勃艮第-弗朗什孔泰大学）；University Hospital of Dijon（第戎大学医院）；CASIS company（CASIS公司）
Cardiac left ventricular (LV) segmentation from short-axis MRI acquired 10 minutes after the injection of a contrast agent (LGE-MRI) is a necessary step in the processing allowing the identification and diagnosis of cardiac diseases such as myocardial infarction. However, this segmentation is challenging due to high variability across subjects and the potential lack of contrast between structures. Then, the main objective of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. To this end, 2.5 D residual neural n…
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…
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…
Lund University（隆德大学）；Skåne University Hospital（斯科讷大学医院）
In this work, the best size for late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) images in the training dataset was determined to optimize deep learning training outcomes. Non-extra pixel and extra pixel interpolation algorithms were used to determine the new size of the LGE-MRI images. A novel strategy was introduced to handle interpolation masks and remove extra class labels in interpolated ground truth (GT) segmentation masks. The expectation maximization, weighted intensity, a priori information (EWA) algorithm was used for quantification of myocardial infarction (MI) in …
Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；Imperial College London（帝国理工学院）；Universitat de Barcelona（巴塞罗那大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Harbin Institute of Technology（哈尔滨工业大学）；Southern University of Science and Technology（南方科技大学）；Technical University of Munich（慕尼黑工业大学）；INRIA（法国国家信息与自动化研究所）；Université Côte d’Azur（蔚蓝海岸大学）；NVIDIA（英伟达）；Xiamen University（厦门大学）；Sichuan University（四川大学）；Tencent AI Lab（腾讯人工智能实验室）
Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suffering from myocardial infarction (MI). Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) provides an important protocol to visualize MI. However, automated segmentation of LGE CMR is still challenging, due to the indistinguishable boundaries, heterogeneous intensity distribution and complex enhancement patterns of pathological myocardium from LGE CMR. Furthermore, compared with the other sequences LG…
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…
Biomedical Image Analysis Group, Imperial College London（伦敦帝国理工学院生物医学图像分析小组）；Data Science Institute, Imperial College London（伦敦帝国理工学院数据科学研究所）；Department of Medicine, Imperial College London（伦敦帝国理工学院医学院）
In this work, we present a fully automatic method to segment cardiac structures from late-gadolinium enhanced (LGE) images without using labelled LGE data for training, but instead by transferring the anatomical knowledge and features learned on annotated balanced steady-state free precession (bSSFP) images, which are easier to acquire. Our framework mainly consists of two neural networks: a multi-modal image translation network for style transfer and a cascaded segmentation network for image segmentation. The multi-modal image translation network generates realistic and diverse synthetic LGE …
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…
In this work, we attempt the segmentation of cardiac structures in late gadolinium-enhanced (LGE) magnetic resonance images (MRI) using only minimal supervision in a two-step approach. In the first step, we register a small set of five LGE cardiac magnetic resonance (CMR) images with ground truth labels to a set of 40 target LGE CMR images without annotation. Each manually annotated ground truth provides labels of the myocardium and the left ventricle (LV) and right ventricle (RV) cavities, which are used as atlases. After multi-atlas label fusion by majority voting, we possess noisy labels fo…
Accurate cardiac computing, analysis and modeling from multi-modality images are important for the diagnosis and treatment of cardiac disease. Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is a promising technique to visualize and quantify myocardial infarction (MI) and atrial scars. Automating quantification of MI and atrial scars can be challenging due to the low image quality and complex enhancement patterns of LGE MRI. Moreover, compared with the other sequences LGE MRIs with gold standard labels are particularly limited, which represents another obstacle for developing …
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）
University of Dundee（邓迪大学）；Technical University of Munich（慕尼黑工业大学）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）；Southern University of Science and Technology（南方科技大学）
Analysis and modeling of the ventricles and myocardium are important in the diagnostic and treatment of heart diseases. Manual delineation of those tissues in cardiac MR (CMR) scans is laborious and time-consuming. The ambiguity of the boundaries makes the segmentation task rather challenging. Furthermore, the annotations on some modalities such as Late Gadolinium Enhancement (LGE) MRI, are often not available. We propose an end-to-end segmentation framework based on convolutional neural network (CNN) and adversarial learning. A dilated residual U-shape network is used as a segmentor to genera…
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…
School of Data Science, Fudan University（复旦大学数据科学学院）
Cardiac segmentation from late gadolinium enhancement MRI is an important task in clinics to identify and evaluate the infarction of myocardium. The automatic segmentation is however still challenging, due to the heterogeneous intensity distributions and indistinct boundaries in the images. In this paper, we propose a new method, based on deep neural networks (DNN), for fully automatic segmentation. The proposed network, referred to as SRSCN, comprises a shape reconstruction neural network (SRNN) and a spatial constraint network (SCN). SRNN aims to maintain a realistic shape of the resulting s…
Fuzhou University（福州大学）；University of Oxford（牛津大学）；Fudan University（复旦大学）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）；Shanghai Medical School, Fudan University（复旦大学上海医学院）；Shanghai Institute of Medical Imaging（上海医学影像研究所）
Myocardial pathology segmentation (MyoPS) is critical for the risk stratification and treatment planning of myocardial infarction (MI). Multi-sequence cardiac magnetic resonance (MS-CMR) images can provide valuable information. For instance, balanced steady-state free precession cine sequences present clear anatomical boundaries, while late gadolinium enhancement and T2-weighted CMR sequences visualize myocardial scar and edema of MI, respectively. Existing methods usually fuse anatomical and pathological information from different CMR sequences for MyoPS, but assume that these images have bee…
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 …
The MYOSAIQ Challenge: Myocardial Segmentation with Automated Infarct Quantification
INSA-Lyon, Universite Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294（里昂国立应用科学学院、里昂第一大学克洛德·贝尔纳大学、圣艾蒂安大学、法国国家科学研究中心、法国国家健康与医学研究院、CREATIS联合研究机构）；Institut Universitaire de France (IUF)（法兰西大学研究院）；University of Sherbrooke（谢布鲁克大学）；Huazhong University of Science and Technology（华中科技大学）；Sunnybrook Research Institute, University of Toronto（多伦多大学桑尼布鲁克研究所）；Imperial College London（伦敦帝国理工学院）；University College London（伦敦大学学院）；Fudan University（复旦大学）；Medical University of Graz（格拉茨医科大学）；Graz University of Technology（格拉茨技术大学）；Universidad de Zaragoza（萨拉戈萨大学）；Instituto de Investigación Sanitaria de Aragón (IIS Aragón)（阿拉贡卫生研究所）；Centre Hospitalier Annecy Genevois（阿讷西日内瓦中心医院）；Hôpital Universitaire Cardiologique Louis Pradel（路易·普拉德尔大学心脏病医院）
Southeast University（东南大学）；Shenzhen University（深圳大学）；Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；Kings College London（伦敦国王学院）；Affiliated Drum Tower Hospital of Nanjing University Medical School（南京大学医学院附属鼓楼医院）
Multi-sequence cardiac magnetic resonance (CMR) provides essential pathology information (scar and edema) to diagnose myocardial infarction. However, automatic pathology segmentation can be challenging due to the difficulty of effectively exploring the underlying information from the multi-sequence CMR data. This paper aims to tackle the scar and edema segmentation from multi-sequence CMR with a novel auto-weighted supervision framework, where the interactions among different supervised layers are explored under a task-specific objective using reinforcement learning. Furthermore, we design a c…
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）
Multi-Source and Multi-Sequence Myocardial Pathology Segmentation Using a Cascading Refinement CNN
Institute of Computer Graphics and Vision, Graz University of Technology（格拉茨工业大学计算机图形学与视觉研究所）；AVL List GmbH（AVL List 有限公司）；Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz（格拉茨医科大学医学信息学、统计学与文献学研究所）
