One of the first steps in the diagnosis of most cardiac diseases, such as pulmonary hypertension, coronary heart disease is the segmentation of ventricles from cardiac magnetic resonance (MRI) images. Manual segmentation of the right ventricle requires diligence and time, while its automated segmentation is challenging due to shape variations and illdefined borders. We propose a deep learning based method for the accurate segmentation of right ventricle, which does not require post-processing and yet it achieves the state-of-the-art performance of 0.86 Dice coefficient and 6.73 mm Hausdorff distance on RVSC-MICCAI 2012 dataset. We use a novel adaptive cost function to counter extreme class-imbalance in the dataset. We present a comprehensive comparative study of loss functions, architectures, and ensembling techniques to build a principled approach for biomedical segmentation tasks.
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）
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 …
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 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…
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…
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…
MorphiNet: A Graph Subdivision Network for Adaptive Bi-ventricle Surface Reconstruction
School of Biomedical Engineering and Imaging Sciences, King’s College London（生物医学工程与影像科学学院，伦敦国王学院）；Brain Imaging & Neuro Epidemiology Department, Luxemboug Institute of Health（脑影像与神经流行病学部门，卢森堡健康研究所）；Department of Biostatistics and Health Informatics, King’s College London（生物统计学与健康信息学系，伦敦国王学院）
Shape of my heart: Cardiac models through learned signed distance functions
Institute for Applied Mathematics, University of Bonn（波恩大学应用数学研究所）；University of Graz（格拉茨大学）；Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile（智利天主教大学生物与医学工程研究所）；Center for Computational Medicine in Cardiology, Università della Svizzera italiana（瑞士意大利语区大学心血管计算医学中心）；FernUni Schweiz（瑞士远程教育大学）；Instituto Cardiocentro Ticino, EOC（提契诺心脏中心研究所（EOC））；University of Trento（特伦托大学）
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. …
Mannheim University of Applied Science（曼海姆应用科技大学）；UT Southwestern Medical Center（德克萨斯大学西南医学中心）；DZHK (German Centre for Cardiovascular Research)（德国心血管研究中心）；German Heart Centre Munich（慕尼黑德国心脏中心）；Hannover Medical School（汉诺威医学院）；Heidelberg University Hospital（海德堡大学医院）
Planning the optimal time of intervention for pulmonary valve replacement surgery in patients with the congenital heart disease Tetralogy of Fallot (TOF) is mainly based on ventricular volume and function according to current guidelines. Both of these two biomarkers are most reliably assessed by segmentation of 3D cardiac magnetic resonance (CMR) images. In several grand challenges in the last years, U-Net architectures have shown impressive results on the provided data. However, in clinical practice, data sets are more diverse considering individual pathologies and image properties derived fr…
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）
University of Amsterdam（阿姆斯特丹大学）；University Medical Center Utrecht（乌得勒支大学医学中心）
Deep learning-based whole-heart segmentation in coronary CT angiography (CCTA) allows the extraction of quantitative imaging measures for cardiovascular risk prediction. Automatic extraction of these measures in patients undergoing only non-contrast-enhanced CT (NCCT) scanning would be valuable. In this work, we leverage information provided by a dual-layer detector CT scanner to obtain a reference standard in virtual non-contrast (VNC) CT images mimicking NCCT images, and train a 3D convolutional neural network (CNN) for the segmentation of VNC as well as NCCT images. Contrast-enhanced acquis…
With the advent of Cardiac Cine Magnetic Resonance (CMR) Imaging, there has been a paradigm shift in medical technology, thanks to its capability of imaging different structures within the heart without ionizing radiation. However, it is very challenging to conduct pre-operative planning of minimally invasive cardiac procedures without accurate segmentation and identification of the left ventricle (LV), right ventricle (RV) blood-pool, and LV-myocardium. Manual segmentation of those structures, nevertheless, is time-consuming and often prone to error and biased outcomes. Hence, automatic and c…
Harbin Institute of Technology（哈尔滨工业大学）；Southern University of Science and Technology（南方科技大学）；CVTE Research（视源研究院）；Cixi Institute of Biomedical Engineering, Chinese Academy of Sciences（中国科学院慈溪生物医学工程研究所）；University of Hong Kong（香港大学）；Guangdong Armed Police Hospital（广东武警医院）
Chest X-rays are the most commonly available and affordable radiological examination for screening thoracic diseases. According to the domain knowledge of screening chest X-rays, the pathological information usually lay on the lung and heart regions. However, it is costly to acquire region-level annotation in practice, and model training mainly relies on image-level class labels in a weakly supervised manner, which is highly challenging for computer-aided chest X-ray screening. To address this issue, some methods have been proposed recently to identify local regions containing pathological inf…
Promise and challenges of heart chamber segmentation from non-contrast CT scans using contrastive unpaired image translation: a feasibility study
Department of Radiology, University of Pittsburgh（匹兹堡大学放射科部门）；Department of Bioengineering, University of Pittsburgh（匹兹堡大学生物工程部门）；Department of Ophthalmology, University of Pittsburgh（匹兹堡大学眼科学部门）
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；National Taiwan University（台湾大学）；University of Southern California（南加州大学）
Assessment of cardiovascular disease (CVD) with cine magnetic resonance imaging (MRI) has been used to non-invasively evaluate detailed cardiac structure and function. Accurate segmentation of cardiac structures from cine MRI is a crucial step for early diagnosis and prognosis of CVD, and has been greatly improved with convolutional neural networks (CNN). There, however, are a number of limitations identified in CNN models, such as limited interpretability and high complexity, thus limiting their use in clinical practice. In this work, to address the limitations, we propose a lightweight and i…
DeepMesh: Mesh-based Cardiac Motion Tracking using Deep Learning
Imperial College London（帝国理工学院）；University of Birmingham（伯明翰大学）；MRC London Institute of Medical Sciences（英国医学研究理事会伦敦医学科学研究所）；Technical University Munich（慕尼黑工业大学）；Klinikum rechts der Isar（伊萨尔河畔临床教学中心）
本文提出 DeepMesh 学习框架，利用多视图二维 CMR 图像重建个体心脏三维网格并估计逐顶点运动场，在 UK Biobank 上优于现有图像与网格类方法。
School of Computation, Information and Technology（计算信息学院）；Technical University of Munich（慕尼黑技术大学）；School of Medicine, Klinikum Rechts der Isar（医学院）；Department of Computer Science（计算机科学系）；University of Sheffield（谢菲尔德大学）；Department of Quantitative Biomedicine（定量生物医学系）；University of Zurich（苏黎世大学）；Department of Computing（计算系）；Imperial College London（伦敦帝国理工学院）；Department of Engineering Science（工程科学系）；University of Oxford（牛津大学）
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…
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…
Purdue University（普渡大学）；Carmel High School（卡梅尔高中）；Case Western Reserve University（凯斯西储大学）；The Pennsylvania State University（宾夕法尼亚州立大学）；Georgia Institute of Technology（佐治亚理工学院）；University at Albany, State University of New York（纽约州立大学奥尔巴尼分校）
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…
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…
Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins Hospital（约翰斯·霍普金斯医院）
Visualizing disease-induced scarring and fibrosis in the heart on cardiac magnetic resonance (CMR) imaging with contrast enhancement (LGE) is paramount in characterizing disease progression and quantifying pathophysiological substrates of arrhythmias. However, segmentation and scar/fibrosis identification from LGE-CMR is an intensive manual process prone to large inter-observer variability. Here, we present a novel fully-automated anatomically-informed deep learning solution for left ventricle (LV) and scar/fibrosis segmentation and clinical feature extraction from LGE-CMR. The technology invo…
Imperial College London（帝国理工学院）；National University of Defense Technology（国防科技大学）；MRC London Institute of Medical Sciences（英国医学研究委员会伦敦医学科学研究所）；Technical University Munich（慕尼黑工业大学）
3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessment of cardiac function and diagnosis of cardiovascular diseases. Most of the previous methods focus on estimating pixel-/voxel-wise motion fields in the full image space, which ignore the fact that motion estimation is mainly relevant and useful within the object of interest, e.g., the heart. In this work, we model the heart as a 3D geometric mesh and propose a novel deep learning-based method that can estimate 3D motion of the heart mesh from 2D short- and long-axis CMR images. By developing a …
The University of Sheffield（谢菲尔德大学）；Universitat Pompeu Fabra（庞培法布拉大学）；University of Oxford（牛津大学）；Queen Mary University of London（伦敦玛丽女王大学）
The exploitation of large-scale population data has the potential to improve healthcare by discovering and understanding patterns and trends within this data. To enable high throughput analysis of cardiac imaging data automatically, a pipeline should comprise quality monitoring of the input images, segmentation of the cardiac structures, assessment of the segmentation quality, and parsing of cardiac functional indexes. We present a fully automatic, high throughput image parsing workflow for the analysis of cardiac MR images, and test its performance on the UK Biobank (UKB) cardiac dataset. The…
