Fully automated segmentation of the left ventricle in cine cardiac MRI using neural network regression

Tan, Li Kuo; McLaughlin, Robert A.; Lim, Einly; Aziz, Yang Faridah Abdul; Liew, Yih Miin

doi:10.1002/jmri.25932

Cited by 74 publications

(44 citation statements)

References 21 publications

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“…In cardiac MRI, ventricular segmentation is one of the fields with more potential for the application of ML models. It makes it possible to quantify the volumetry and improve the efficiency and reproducibility of clinical assessments [ 64 – 66 ]. Avendi et al [ 65 ] used deep learning algorithms (i.e., convolutional neural networks and stacked autoencoders) trained through cardiac MRI datasets, for the automatic detection and segmentation of right ventricular (RV) chamber foreseeing the accuracy of these algorithms.…”

Section: Applications In Cardiovascular Imagingmentioning

confidence: 99%

“…Avendi et al [ 65 ] used deep learning algorithms (i.e., convolutional neural networks and stacked autoencoders) trained through cardiac MRI datasets, for the automatic detection and segmentation of right ventricular (RV) chamber foreseeing the accuracy of these algorithms. Likewise, for left ventricular segmentation, several automated neural networks have been successfully developed, especially for cardiac cine MRI [ 66 – 68 ]. Another application of ML in cardiac MRI takes place in the detection of subacute or chronic myocardial scar [ 69 ].…”

Section: Applications In Cardiovascular Imagingmentioning

confidence: 99%

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Artificial Intelligence (AI) and Cardiovascular Diseases: An Unexpected Alliance

Romiti

Vinciguerra

Saade

et al. 2020

Cardiology Research and Practice

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Cardiovascular disease (CVD), despite the significant advances in the diagnosis and treatments, still represents the leading cause of morbidity and mortality worldwide. In order to improve and optimize CVD outcomes, artificial intelligence techniques have the potential to radically change the way we practice cardiology, especially in imaging, offering us novel tools to interpret data and make clinical decisions. AI techniques such as machine learning and deep learning can also improve medical knowledge due to the increase of the volume and complexity of the data, unlocking clinically relevant information. Likewise, the use of emerging communication and information technologies is becoming pivotal to create a pervasive healthcare service through which elderly and chronic disease patients can receive medical care at their home, reducing hospitalizations and improving quality of life. The aim of this review is to describe the contemporary state of artificial intelligence and digital health applied to cardiovascular medicine as well as to provide physicians with their potential not only in cardiac imaging but most of all in clinical practice.

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Section: Applications In Cardiovascular Imagingmentioning

confidence: 99%

Section: Applications In Cardiovascular Imagingmentioning

confidence: 99%

Artificial Intelligence (AI) and Cardiovascular Diseases: An Unexpected Alliance

Romiti

Vinciguerra

Saade

et al. 2020

Cardiology Research and Practice

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show abstract

“…Deep learning algorithms have been generated that approximate the cardiac measurements of expert readers. [25][26][27]48,49 One such algorithm proposed by Avendi et al 25 was well correlated with ground-truth measurements (0.99 for end systolic and 0.98 for end diastolic). Most recently, several commercial vendors have begun to take an interest in this technology and to integrate these algorithms into their software.…”

Section: Applications To Cardiovascular Diseasementioning

confidence: 99%

“…Ortiz et al 30 used neural networks to analyze cardiac contractility to predict 1-year mortality in patients with heart failure. Since this early work, supervised machine learning 26 RV, LV endocardium and epicardium CNN Tan et al 27 LV segmentation ANN Baessler et al 28 Myocardial scar detection Random forests Dawes et al 29 Pulmonary hypertension prognosis PCA ECHO Ortiz et al 30 HF prognosis ANN Narula et al 31 HCM vs athlete's heart SVM, Random forests, ANN Sengupta et al 32 Constrictive pericarditis vs restrictive cardiomyopathy AMC, random forest, k-NN, SVM Sengur 33 Valvular disease SVM Moghaddasi and Nourian 34 MR severity SVM Vidya et al 35 MI detection SVM CT Wolterink et al 36 CAC scoring CNN Isgum et al 37 CAC scoring k-NN, SVM Itu et al 38 FFR estimation deep neural network Motwani et al 39 Prognosis Logistic regression Mannil et al 40 MI detection Decision tree, k-NN, random forest, ANN 32 diagnose valvular heart disease, 33 grade severity of mitral valve regurgitation, 34 automate ejection fraction measurement, 53 and detect the presence of myocardial infarction. 35,54 Several machine learning applications have also been developed to assist in the interpretation of CT. For example, algorithms have been developed for the automation of coronary artery calcium scoring 36,37,55,56 and assessment of the functional significance of coronary lesions.…”

Section: Applications To Cardiovascular Diseasementioning

confidence: 99%

Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging

Retson

Besser

Sall³

et al. 2019

Journal of Thoracic Imaging

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Advances in technology have always had the potential and opportunity to shape the practice of medicine, and in no medical specialty has technology been more rapidly embraced and adopted than radiology. Machine learning and deep neural networks promise to transform the practice of medicine, and, in particular, the practice of diagnostic radiology. These technologies are evolving at a rapid pace due to innovations in computational hardware and novel neural network architectures. Several cutting-edge postprocessing analysis applications are actively being developed in the fields of thoracic and cardiovascular imaging, including applications for lesion detection and characterization, lung parenchymal characterization, coronary artery assessment, cardiac volumetry and function, and anatomic localization. Cardiothoracic and cardiovascular imaging lies at the technological forefront of radiology due to a confluence of technical advances. Enhanced equipment has enabled computed tomography and magnetic resonance imaging scanners that can safely capture images that freeze the motion of the heart to exquisitely delineate fine anatomic structures. Computing hardware developments have enabled an explosion in computational capabilities and in data storage. Progress in software and fluid mechanical models is enabling complex 3D and 4D reconstructions to not only visualize and assess the dynamic motion of the heart, but also quantify its blood flow and hemodynamics. And now, innovations in machine learning, particularly in the form of deep neural networks, are enabling us to leverage the increasingly massive data repositories that are prevalent in the field. Here, we discuss developments in machine learning techniques and deep neural networks to highlight their likely role in future radiologic practice, both in and outside of image interpretation and analysis. We discuss the concepts of validation, generalizability, and clinical utility, as they pertain to this and other new technologies, and we reflect upon the opportunities and challenges of bringing these into daily use.

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“…Zhen et al [6] applied unsupervised representation learning and regression forests for the direct estimation of ventricular volume. Avendi et al [7] combined convolutional neural network learning and a deformable shape model to segment the left ventricle for volume and ejection fraction estimation. Tan et al [8] developed and validated a fully automated neural network regression-based algorithm for segmentation of the LV, with full coverage from apex to base across all cardiac phases, utilizing both SA and long-axis LA scans.…”

Section: Introductionmentioning

confidence: 99%

Fully Automated Quantification of Cardiac Indices from Cine MRI Using a Combination of Convolution Neural Networks

Pereira

Rebelo

Moreno

et al. 2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Cardiovascular magnetic resonance imaging (CMRI) is one of the most accurate non-invasive modalities for evaluation of cardiac function, especially the left ventricle (LV). In this modality, the manual or semi-automatic delineation of LV by experts is currently the standard clinical practice for chambers segmentation. Despite these efforts, global quantification of LV remains a challenge. In this work, a combination of two convolutional neural network (CNN) architectures for quantitative evaluation of the LV is described, which estimates the cavity and the myocardium areas, endocardial cavity dimensions in three directions, and the myocardium regional wall thickness in six radial directions. The method was validated in CMRI exams of 56 patients (LVQuan19 dataset) and evaluated by metrics Dice Index, Mean Absolute Error, and Correlation with superior performance compared to the state-of-the-art methods. The combination of the CNN architectures provided a simpler yet fully automated approach, requiring no specialist interaction. Clinical Relevance-With the proposed method, it is possible to perform automatically the full quantification of regional clinically relevant parameters of the left ventricle in short-axis CMRI images with superior performance compared to state-ofthe-art methods.

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Fully automated segmentation of the left ventricle in cine cardiac MRI using neural network regression

Abstract: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Cited by 74 publications

References 21 publications

Artificial Intelligence (AI) and Cardiovascular Diseases: An Unexpected Alliance

Artificial Intelligence (AI) and Cardiovascular Diseases: An Unexpected Alliance

Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging

Fully Automated Quantification of Cardiac Indices from Cine MRI Using a Combination of Convolution Neural Networks

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