Segmentation of left atrium using CT images and a deep learning model.

The left atrium (LA) is one of the cardiac cavities with the most complex anatomical structures. Its role in the clinical diagnosis and patient's management is critical, as it is responsible for the atrial fibrillation, a condition that promotes the thrombogenesis inside the left atrial appendage. The development of an automated approach for LA segmentation is a demanding task mainly due to its anatomical complexity and the variation of its shape among patients. In this study, we focus to develop an unbiased pipeline capable to segment the atrial cavity from CT images. For evaluation purposes state-of-the-art metrics were used to assess the segmentation results. Particularly, the results indicated the mean values of the dice score 80%, the hausdorff distance 11.78mm, the average surface distance 2.24mm and the rand error index 0.2.

Comparison of three U-Net family architectures for left ventricular myocardial wall automatic segmentation.

Left ventricular (LV) segmentation is an important process which can provide quantitative clinical measurements such as volume, wall thickness and ejection fraction. The development of an automatic LV segmentation procedure is a challenging and complicated task mainly due to the variation of the heart shape from patient to patient, especially for those with pathological and physiological changes. In this study, we focus on the implementation, evaluation and comparison of three different Deep Learning architectures of the U-Net family: the custom 2-D U-Net, the ResU-Net++ and the DenseU-Net, in order to segment the LV myocardial wall. Our approach was applied to cardiac CT datasets specifically derived from patients with hypertrophic cardiomyopathy. The results of the models demonstrated high performance in the segmentation process with minor losses. The model revealed a dice score for U-Net, Res-U-net++ and Dense U-Net, 0.81, 0.82 and 0.84, respectively.

A computational pipeline for data augmentation towards the improvement of disease classification and risk stratification models: A case study in two clinical domains.

Virtual population generation is an emerging field in data science with numerous applications in healthcare towards the augmentation of clinical research databases with significant lack of population size. However, the impact of data augmentation on the development of AI (artificial intelligence) models to address clinical unmet needs has not yet been investigated. In this work, we assess whether the aggregation of real with virtual patient data can improve the performance of the existing risk stratification and disease classification models in two rare clinical domains, namely the primary Sjögren's Syndrome (pSS) and the hypertrophic cardiomyopathy (HCM), for the first time in the literature. To do so, multivariate approaches, such as, the multivariate normal distribution (MVND), and straightforward ones, such as, the Bayesian networks, the artificial neural networks (ANNs), and the tree ensembles are compared against their performance towards the generation of high-quality virtual data. Both boosting and bagging algorithms, such as, the Gradient boosting trees (XGBoost), the AdaBoost and the Random Forests (RFs) were trained on the augmented data to evaluate the performance improvement for lymphoma classification and HCM risk stratification. Our results revealed the favorable performance of the tree ensemble generators, in both domains, yielding virtual data with goodness-of-fit 0.021 and KL-divergence 0.029 in pSS and 0.029, 0.027 in HCM, respectively. The application of the XGBoost on the augmented data revealed an increase by 10.9% in accuracy, 10.7% in sensitivity, 11.5% in specificity for lymphoma classification and 16.1% in accuracy, 16.9% in sensitivity, 13.7% in specificity in HCM risk stratification.

Wall shear stress alterations at left atrium and left atrial appendage employing abnormal blood velocity profiles.

In this study, we developed and analyzed different patient-specific 3D anatomical models of the left atrium including left atrial Appendage, in order to investigate the local hemodynamics. Particularly, we focused on the left atrial appendage and its impact on thrombus formation due to wall shear stress alterations. A 3D semi-automated reconstruction approach was carried out to segment and reconstruct the left atrium from CT scans. Six different patients were studied applying their patient-specific clinical data. Three different velocity profiles simulated for each patient case, representing one normal and two abnormal conditions. Simulations varied significantly according to different appendage morphologies. Our scope is to describe the hemodynamic behavior at the left atrium and the left atrial appendage according to different blood velocities based on their anatomic variety (chicken wing 0.14 m/s, windsock 0.10, cactus 0.08, and cauliflower 0.04). Wall shear stress results were demonstrated and correlated with the velocities and the thrombus formation inside the appendage cavity.