Segmentation of 4D Flow MRI: Comparison between 3D Deep Learning and Velocity-Based Level Sets.

A thoracic aortic aneurysm is an abnormal dilatation of the aorta that can progress and lead to rupture. The decision to conduct surgery is made by considering the maximum diameter, but it is now well known that this metric alone is not completely reliable. The advent of 4D flow magnetic resonance imaging has allowed for the calculation of new biomarkers for the study of aortic diseases, such as wall shear stress. However, the calculation of these biomarkers requires the precise segmentation of the aorta during all phases of the cardiac cycle. The objective of this work was to compare two different methods for automatically segmenting the thoracic aorta in the systolic phase using 4D flow MRI. The first method is based on a level set framework and uses the velocity field in addition to 3D phase contrast magnetic resonance imaging. The second method is a U-Net-like approach that is only applied to magnitude images from 4D flow MRI. The used dataset was composed of 36 exams from different patients, with ground truth data for the systolic phase of the cardiac cycle. The comparison was performed based on selected metrics, such as the Dice similarity coefficient (DSC) and Hausdorf distance (HD), for the whole aorta and also three aortic regions. Wall shear stress was also assessed and the maximum wall shear stress values were used for comparison. The U-Net-based approach provided statistically better results for the 3D segmentation of the aorta, with a DSC of 0.92 ± 0.02 vs. 0.86 ± 0.5 and an HD of 21.49 ± 24.8 mm vs. 35.79 ± 31.33 mm for the whole aorta. The absolute difference between the wall shear stress and ground truth slightly favored the level set method, but not significantly (0.754 ± 1.07 Pa vs. 0.737 ± 0.79 Pa). The results showed that the deep learning-based method should be considered for the segmentation of all time steps in order to evaluate biomarkers based on 4D flow MRI.

Automatic classification of patients with myocardial infarction or myocarditis based only on clinical data: A quick response.

BACKGROUND: In acute cardiovascular disease management, the delay between the admission in a hospital emergency department and the assessment of the disease from a Delayed Enhancement cardiac MRI (DE-MRI) scan is one of the barriers for an immediate management of patients with suspected myocardial infarction or myocarditis. OBJECTIVES: This work targets patients who arrive at the hospital with chest pain and are suspected of having a myocardial infarction or a myocarditis. The main objective is to classify these patients based solely on clinical data in order to provide an early accurate diagnosis. METHODS: Machine learning (ML) and ensemble approaches have been used to construct a framework to automatically classify the patients according to their clinical conditions. 10-fold cross-validation is used during the model's training to avoid overfitting. Approaches such as Stratified, Over-sampling, Under-sampling, NearMiss, and SMOTE were tested in order to address the imbalance of the data (i.e. proportion of cases per pathology). The ground truth is provided by a DE-MRI exam (normal exam, myocarditis or myocardial infarction). RESULTS: The stacked generalization technique with Over-sampling seems to be the best one providing more than 97% of accuracy corresponding to 11 wrong classifications among 537 cases. Generally speaking, ensemble classifiers such as Stacking provided the best prediction. The five most important features are troponin, age, tobacco, sex and FEVG calculated from echocardiography. CONCLUSION: Our study provides a reliable approach to classify the patients in emergency department between myocarditis, myocardial infarction or other patient condition from only clinical information, considering DE-MRI as ground-truth. Among the different machine learning and ensemble techniques tested, the stacked generalization technique is the best one providing an accuracy of 97.4%. This automatic classification could provide a quick answer before imaging exam such as cardiovascular MRI depending on the patient's condition.

Comparison of In-Vivo and Ex-Vivo Ascending Aorta Elastic Properties through Automatic Deep Learning Segmentation of Cine-MRI and Biomechanical Testing.

Ascending aortic aneurysm is a pathology that is important to be supervised and treated. During the years the aorta dilates, it becomes stiff, and its elastic properties decrease. In some cases, the aortic wall can rupture leading to aortic dissection with a high mortality rate. The main reference standard to measure when the patient needs to undertake surgery is the aortic diameter. However, the aortic diameter was shown not to be sufficient to predict aortic dissection, implying other characteristics should be considered. Therefore, the main objective of this work is to assess in-vivo the elastic properties of four different quadrants of the ascending aorta and compare the results with equivalent properties obtained ex-vivo. The database consists of 73 cine-MRI sequences of thoracic aorta acquired in axial orientation at the level of the pulmonary trunk. All the patients have dilated aorta and surgery is required. The exams were acquired just prior to surgery, each consisting of 30 slices on average across the cardiac cycle. Multiple deep learning architectures have been explored with different hyperparameters and settings to automatically segment the contour of the aorta on each image and then automatically calculate the aortic compliance. A semantic segmentation U-Net network outperforms the rest explored networks with a Dice score of 98.09% (±0.96%) and a Hausdorff distance of 4.88 mm (±1.70 mm). Local aortic compliance and local aortic wall strain were calculated from the segmented surfaces for each quadrant and then compared with elastic properties obtained ex-vivo. Good agreement was observed between Young's modulus and in-vivo strain. Our results suggest that the lateral and posterior quadrants are the stiffest. In contrast, the medial and anterior quadrants have the lowest aortic stiffness. The in-vivo stiffness tendency agrees with the values obtained ex-vivo. We can conclude that our automatic segmentation method is robust and compatible with clinical practice (thanks to a graphical user interface), while the in-vivo elastic properties are reliable and compatible with the ex-vivo ones.

Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge.

A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization therapy. Delayed enhancement-MRI or DE-MRI, which is performed 10 min after injection of the contrast agent, provides high contrast between viable and nonviable myocardium and is therefore a method of choice to evaluate the extent of MI. To automatically assess myocardial status, the results of the EMIDEC challenge that focused on this task are presented in this paper. The challenge's main objectives were twofold. First, to evaluate if deep learning methods can distinguish between non-infarct and pathological exams, i.e. exams with or without hyperenhanced area. Second, to automatically calculate the extent of myocardial infarction. The publicly available database consists of 150 exams divided into 50 cases without any hyperenhanced area after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department. Along with MRI, clinical characteristics are also provided. The obtained results issued from several works show that the automatic classification of an exam is a reachable task (the best method providing an accuracy of 0.92), and the automatic segmentation of the myocardium is possible. However, the segmentation of the diseased area needs to be improved, mainly due to the small size of these areas and the lack of contrast with the surrounding structures.

Prediction of Myocardial Infarction From Patient Features With Machine Learning.

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.

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

Delayed Enhancement cardiac MRI (DE-MRI) has become indispensable for the diagnosis of myocardial diseases. However, to quantify the disease severity, doctors need time to manually annotate the scar and myocardium. To address this issue, in this paper we propose an automatic myocardial infarction segmentation approach on the left ventricle from short-axis DE-MRI based on Convolutional Neural Networks (CNN). The objective is to segment myocardial infarction on short-axis DE-MRI images of the left ventricle acquired 10 min after the injection of a gadolinium-based contrast agent. The segmentation of the infarction area is realized in two stages: a first CNN model finds the contour of myocardium and a second CNN model segments the infarction. Compared to the manual intra-observer and inter-observer variations for the segmentation of myocardial infarction, and to the automatic segmentation with Gaussian Mixture Model, our proposal achieves satisfying segmentation results on our dataset of 904 DE-MRI slices.