scholarly journals Deep learning based fully automatic segmentation of the left ventricular endocardium and epicardium from cardiac cine MRI

2021 ◽  
Vol 11 (4) ◽  
pp. 1600-1612
Author(s):  
Yan Wang ◽  
Yue Zhang ◽  
Zhaoying Wen ◽  
Bing Tian ◽  
Evan Kao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Segmentation ◽  
Left Ventricular ◽  
Cine Mri ◽  
Cardiac Cine ◽  
Fully Automatic

scholarly journals High interstudy repeatability of automatic deep learnt biventricular CMR measurements

2021 ◽  
Vol 22 (Supplement_2) ◽  
Author(s):  
S Alabed ◽  
K Karunasaagarar ◽  
F Alandejani ◽  
P Garg ◽  
J Uthoff ◽  
...  
Keyword(s):  
Pulmonary Hypertension ◽  
Deep Learning ◽  
Automatic Segmentation ◽  
Intraclass Correlation ◽  
Therapy Response ◽  
Left Ventricular ◽  
Dice Similarity Coefficient ◽  
Learning Approaches ◽  
Fully Automatic ◽  
Automatic Contouring

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): Wellcome Trust (UK), NIHR (UK) Introduction Cardiac magnetic resonance (CMR) measurements have significant diagnostic and prognostic value. Accurate and repeatable measurements are essential to assess disease severity, evaluate therapy response and monitor disease progression. Deep learning approaches have shown promise for automatic left ventricular (LV) segmentation on CMR, however fully automatic right ventricular (RV) segmentation remains challenging. We aimed to develop a biventricular automatic contouring model and evaluate the interstudy repeatability of the model in a prospectively recruited cohort. Methods A deep learning CMR contouring model was developed in a retrospective multi-vendor (Siemens and General Electric), multi-pathology cohort of patients, predominantly with heart failure, pulmonary hypertension and lung diseases (n = 400, ASPIRE registry). Biventricular segmentations were made on all CMR studies across cardiac phases. To test the accuracy of the automatic segmentation, 30 ASPIRE CMRs were segmented independently by two CMR experts. Each segmentation was compared to the automatic contouring with agreement assessed using the Dice similarity coefficient (DSC).  A prospective validation cohort of 46 subjects (10 healthy volunteers and 36 patients with pulmonary hypertension) were recruited to assess interstudy agreement of automatic and manual CMR assessments. Two CMR studies were performed during separate sessions on the same day. Interstudy repeatability was assessed using intraclass correlation coefficient (ICC) and Bland-Altman plots.  Results DSC showed high agreement (figure 1) comparing automatic and expert CMR readers, with minimal bias towards either CMR expert. The scan-scan repeatability CMR measurements were higher for all automatic RV measurements (ICC 0.89 to 0.98) compared to manual RV measurements (0.78 to 0.98). LV automatic and manual measurements were similarly repeatable (figure 2). Bland-Altman plots showed strong agreement with small mean differences between the scan-scan measurements (figure 2). Conclusion Fully automatic biventricular short-axis segmentations are comparable with expert manual segmentations, and have shown excellent interstudy repeatability.

Automatic segmentation of left ventricle in cardiac cine MRI images based on deep learning

2017 ◽  
Author(s):  
Tian Zhou ◽  
Ilknur Icke ◽  
Belma Dogdas ◽  
Sarayu Parimal ◽  
Smita Sampath ◽  
...  
Keyword(s):  
Deep Learning ◽  
Left Ventricle ◽  
Automatic Segmentation ◽  
Cine Mri ◽  
Cardiac Cine

Fully-automatic left ventricular segmentation from long-axis cardiac cine MR scans

2017 ◽  
Vol 39 ◽  
pp. 44-55 ◽  
Author(s):  
Rahil Shahzad ◽  
Qian Tao ◽  
Oleh Dzyubachyk ◽  
Marius Staring ◽  
Boudewijn P.F. Lelieveldt ◽  
...  
Keyword(s):  
Left Ventricular ◽  
Cine Mr ◽  
Cardiac Cine ◽  
Fully Automatic

scholarly journals Echocardiographic Features of Patients with Coronary Heart Disease and Angina Pectoris under Deep Learning Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xianjing Han ◽  
Guoxin Liang
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Deep Learning ◽  
Left Ventricle ◽  
Automatic Segmentation ◽  
Learning Algorithms ◽  
Left Ventricular ◽  
Comprehensive Analysis ◽  
Practical Application ◽  
Normal People

Based on the VGG19-fully convolutional network (FCN) (VGG19-FCN) and U-Net model in the deep learning algorithms, the left ventricle in the ultrasonic cardiogram was segmented automatically. In addition, this study evaluated the value of ultrasonic cardiogram features after segmentation by the optimized algorithm in diagnosing patients with coronary heart disease (CHD) and angina pectorisody; patients with arrhythmia; and pa. In this study, 30 patients with confirmed CHD and 30 normal people without CHD from the same hospital in a certain area were selected as the research objects. Firstly, the VGG19-FCN and U-Net model algorithms were selected to automatically segment the left ventricular part of the apical four-chamber static image, which was realized through the weights of the fine-tune basic model algorithm. Subsequently, the experimental subjects were divided into a normal group and a CHD group, and the data were obtained through the ultrasonic cardiogram feature analysis of automatic segmentation by the algorithm. The differences in the ejection fraction (EF), left ventricular fractional shortening (FS), and E/A values (in early and late of the diastolic phase) of the left ventricle for patients in the two groups were compared. In addition, the ultrasonic cardiogram left ventricular segmentation results of normal people and patients with CHD were compared. A comprehensive analysis suggested that the U-Net model was more suitable for the practical application of automatic ultrasonic cardiogram segmentation. According to the analyzed data results, the global systolic function parameters (EF, FS, and E/A values) of the left ventricle for patients showed statistically obvious differences ( P < 0.05 ). In summary, deep learning algorithms can effectively improve the efficiency of ultrasonic cardiogram left ventricular segmentation, show a great role in the diagnosis of CHD patients, and provide a reliable theoretical basis and foundation research on the subsequent CHD imaging diagnosis. The comprehensive analysis showed that the U-Net model was more suitable for the practical application of echocardiographic automatic segmentation, and this study can effectively improve the efficiency of echocardiographic left ventricular segmentation, which played an important role in the diagnosis of coronary heart disease, providing a reliable theoretical basis and foundation for subsequent CHD imaging research.

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