Automatic deep learning-based myocardial infarction segmentation from delayed enhancement MRI

Chen, Zhihao; Lalande, Alain; Salomon, Michel; Decourselle, Thomas; Pommier, Thibaut; Qayyum, Abdul; Shi, Jixi; Perrot, Gilles; Couturier, Raphaël

doi:10.1016/j.compmedimag.2021.102014

Cited by 29 publications

(16 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the quantification task, the ensemble method achieved the best predicted PIM, which showed only 0.056 of error for the PIM comparing with the DE-MRI ground truth. By compared these results with the inter-and intra-observer variation studies done on equivalent data by Chen et al (35), we can conclude that our method provides results with the same order of error as between experts. Indeed, they found variability of 8.8 and 11% for the intra-and inter-observer variations, respectively.…”

Section: Discussionmentioning

confidence: 66%

See 1 more Smart Citation

Prediction of Myocardial Infarction From Patient Features With Machine Learning

Chen

Shi

Pommier

et al. 2022

Front. Cardiovasc. Med.

Self Cite

View full text Add to dashboard Cite

This study proposes machine learning-based models to automatically evaluate the severity of myocardial infarction (MI) from physiological, clinical, and paraclinical features. Two types of machine learning models are investigated for the MI assessment: the classification models classify the presence of the infarct and the persistent microvascular obstruction (PMO), and the regression models quantify the Percentage of Infarcted Myocardium (PIM) of patients suspected of having an acute MI during their reception in the emergency department. The ground truth labels for these supervised models are derived from the corresponding Delayed Enhancement MRI (DE-MRI) exams and manual annotations of the myocardium and scar tissues. Experiments were conducted on 150 cases and evaluated with cross-validation. Results showed that for the MI (PMO inclusive) and the PMO (infarct exclusive), the best models obtained respectively a mean error of 0.056 and 0.012 for the quantification, and 88.67 and 77.33% for the classification accuracy of the state of the myocardium. The study of the features' importance also revealed that the troponin value had the strongest correlation to the severity of the MI among the 12 selected features. For the proposal's translational perspective, in cardiac emergencies, qualitative and quantitative analysis can be obtained prior to the achievement of MRI by relying only on conventional tests and patient features, thus, providing an objective reference for further treatment by physicians.

show abstract

Section: Discussionmentioning

confidence: 66%

“…One limitation of this study is that the ground truth relies on manual annotation. Chen et al found an inter-observer variability of 11% on the same type of data (35). This imprecision certainly affects the results of the models and must be reduced.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Myocardial Infarction From Patient Features With Machine Learning

Chen

Shi

Pommier

et al. 2022

Front. Cardiovasc. Med.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Infarct size/ late gadolinium enhancement Chen et al proposed an automatic MI segmentation approach based on CNNs to analyse CMR LGE sequences. (30) Their proposal demonstrated promising segmentation results when compared to the intraobserver and interobserver variations in manual segmentation, and to automatic segmentation with Gaussian Mixture Model. Engan et al designed an experimental framework for data exploration which involved computing a very large number of features to describe the characteristics of the regions of interest in the images and found that the addition of texture analysis can improve the discriminative power of scarred and non-scarred myocardium to distinguish between patients with high and low risk of serious ventricular arrhythmias post MI.…”

Section: Image Segmentationmentioning

confidence: 99%

Artificial Intelligence and Cardiovascular Magnetic Resonance Imaging in Myocardial Infarction Patients

Chong

Abdulkareem

Petersen

et al. 2022

Current Problems in Cardiology

View full text Add to dashboard Cite

“…Early detection of MI is crucial for effective diagnosis and therapy to alleviate the MI risk that leads to death. Several techniques have been used for assessing MI include electrocardiogram (ECG) [3]- [5] computed tomography (CT) scan [6], [7] and magnetic resonance imaging (MRI) [8]- [11] In particular, cardiac MRI is the gold standard modality for assessing myocardial tissue providing comprehensive information on the myocardium's structures and functions [12]. Segmentation approaches are widely used in clinical CMR analysis to delineate the healthy and pathological contours of LV and myocardium.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images

Shaaf

Jamil²,

Ambar³

2023

Int. J. Onl. Eng.

View full text Add to dashboard Cite

Cardiovascular diseases (CVDs) are considered one of the leading causes of death worldwide. Myocardial infarction (MI) is one of the deadliest cardiac diseases that require more consideration. Recently, cardiac magnetic resonance imaging (MRI) has been applied as a standard technique for assessing such diseases. The segmentation of the left ventricle (LV) and myocardium from MRI images is vital in detecting MI disease at its early stages. The automatic segmentation of LV is still challenging due to the complex structures of MRI images, inhomogeneous LV shape and moving organs around the LV, such as the lungs and diaphragm. Thus, this study proposed a convolutional neural network (CNN) model for LV and myocardium segmentation to detect MI. The layers selection and hyper-parameters fine-tuning were applied before the training phase. The model showed robust performance based on the evaluation metrics such as accuracy, sensitivity, specificity, dice score coefficient (DSC), Jaccard index and intersection over union (IOU) with values of 0.86, 0.91, 0.84, 0.81, 0.69 and 0.83, respectively.

show abstract

Automatic deep learning-based myocardial infarction segmentation from delayed enhancement MRI

Cited by 29 publications

References 19 publications

Prediction of Myocardial Infarction From Patient Features With Machine Learning

Prediction of Myocardial Infarction From Patient Features With Machine Learning

Artificial Intelligence and Cardiovascular Magnetic Resonance Imaging in Myocardial Infarction Patients

Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images

Contact Info

Product

Resources

About