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Accurate segmentation of the aorta and its associated arch branches is crucial for diagnosing aortic diseases. While deep learning techniques have significantly improved aorta segmentation, they remain challenging due to the intricate multiscale structure and the complexity of the surrounding tissues. This paper presents a novel approach for enhancing aorta segmentation using a Bayesian neural network-based hierarchical Laplacian of Gaussian (LoG) model. Our model consists of a 3D U-Net stream and a hierarchical LoG stream: the former provides an initial aorta segmentation, and the latter enhances blood vessel detection across varying scales by learning suitable LoG kernels, enabling self-adaptive handling of different parts of the aorta vessels with significant scale differences. We employ a Bayesian method to parameterize the LoG stream and provide confidence intervals for the segmentation results, ensuring robustness and reliability of the prediction for vascular medical image analysts. Experimental results show that our model can accurately segment main and supra-aortic vessels, yielding at least a 3% gain in the Dice coefficient over state-of-the-art methods across multiple vo

The aorta, the body's largest artery, is prone to pathologies such as dissection, aneurysm, and atherosclerosis, which often require timely intervention. Minimally invasive repairs involving branch vessels necessitate detailed 3D anatomical analysis. Existing methods often overlook hierarchical anatomical relationships while struggling with severe class imbalance inherent in vascular structures. We address these challenges with a curriculum learning strategy that leverages a novel fractal softmax for hierarchical semantic learning. Inspired by human cognition, our approach progressively learns anatomical constraints by decomposing complex structures from simple to complex components. The curriculum learning framework naturally addresses class imbalance by first establishing robust feature representations for dominant classes before tackling rare but anatomically critical structures, significantly accelerating model convergence in multi-class scenarios. Our two-stage inference strategy achieves up to fivefold acceleration, enhancing clinical practicality. On the validation set at epoch 50, our hierarchical semantic loss improves the Dice score of nnU-Net ResEnc M by 11.65%. The prop

The segmentation of the abdominal aorta in non-contrast CT images is a non-trivial task for computer-assisted endovascular navigation, particularly in scenarios where contrast agents are unsuitable. While state-of-the-art deep learning segmentation models have been proposed recently for this task, they are trained on manually annotated strong labels. However, the inherent ambiguity in the boundary of the aorta in non-contrast CT may undermine the reliability of strong labels, leading to potential overfitting risks. This paper introduces a Gaussian-based pseudo label, integrated into conventional deep learning models through deep supervision, to achieve Morphological Attention (MA) enhancement. As the Gaussian pseudo label retains the morphological features of the aorta without explicitly representing its boundary distribution, we suggest that it preserves aortic morphology during training while mitigating the negative impact of ambiguous boundaries, reducing the risk of overfitting. It is introduced in various 2D/3D deep learning models and validated on our local data set of 30 non-contrast CT volumes comprising 5749 CT slices. The results underscore the effectiveness of MA in pres

Synthetic data generation plays a crucial role in medical research by mitigating privacy concerns and enabling large-scale patient data analysis. This study presents a beta-Variational Autoencoder Graph Convolutional Neural Network framework for generating synthetic Abdominal Aorta Aneurysms (AAA). Using a small real-world dataset, our approach extracts key anatomical features and captures complex statistical relationships within a compact disentangled latent space. To address data limitations, low-impact data augmentation based on Procrustes analysis was employed, preserving anatomical integrity. The generation strategies, both deterministic and stochastic, manage to enhance data diversity while ensuring realism. Compared to PCA-based approaches, our model performs more robustly on unseen data by capturing complex, nonlinear anatomical variations. This enables more comprehensive clinical and statistical analyses than the original dataset alone. The resulting synthetic AAA dataset preserves patient privacy while providing a scalable foundation for medical research, device testing, and computational modeling.

Advancements in medical imaging and endovascular grafting have facilitated minimally invasive treatments for aortic diseases. Accurate 3D segmentation of the aorta and its branches is crucial for interventions, as inaccurate segmentation can lead to erroneous surgical planning and endograft construction. Previous methods simplified aortic segmentation as a binary image segmentation problem, overlooking the necessity of distinguishing between individual aortic branches. In this paper, we introduce Context Infused Swin-UNet (CIS-UNet), a deep learning model designed for multi-class segmentation of the aorta and thirteen aortic branches. Combining the strengths of Convolutional Neural Networks (CNNs) and Swin transformers, CIS-UNet adopts a hierarchical encoder-decoder structure comprising a CNN encoder, symmetric decoder, skip connections, and a novel Context-aware Shifted Window Self-Attention (CSW-SA) as the bottleneck block. Notably, CSW-SA introduces a unique utilization of the patch merging layer, distinct from conventional Swin transformers. It efficiently condenses the feature map, providing a global spatial context and enhancing performance when applied at the bottleneck laye

The effects of the aortic geometry on its mechanics and blood flow, and subsequently on aortic pathologies, remain largely unexplored. The main obstacle lies in obtaining patient-specific aorta models, an extremely difficult procedure in terms of ethics and availability, segmentation, mesh generation, and all of the accompanying processes. Contrastingly, idealized models are easy to build but do not faithfully represent patient-specific variability. Additionally, a unified aortic parametrization in clinic and engineering has not yet been achieved. To bridge this gap, we introduce a new set of statistical parameters to generate synthetic models of the aorta. The parameters possess geometric significance and fall within physiological ranges, effectively bridging the disciplines of clinical medicine and engineering. Smoothly blended realistic representations are recovered with convolution surfaces. These enable high-quality visualization and biological appearance, whereas the structured mesh generation paves the way for numerical simulations. The only requirement of the approach is one patient-specific aorta model and the statistical data for parameter values obtained from the literat

Aorta provides the main blood supply of the body. Screening of aorta with imaging helps for early aortic disease detection and monitoring. In this work, we describe our solution to the Segmentation of the Aorta (SEG.A.231) from 3D CT challenge. We use automated segmentation method Auto3DSeg available in MONAI. Our solution achieves an average Dice score of 0.920 and 95th percentile of the Hausdorff Distance (HD95) of 6.013, which ranks first and wins the SEG.A. 2023 challenge.

Fluid-structure interaction (FSI) modeling, a technique widely used to enhance imaging modalities for adult and pediatric heart diseases, has been underutilized in the context of fetal circulation because of limited data on flow conditions and material properties. Recognizing the significant impact of congenital heart diseases on the fetal aorta, our research aims to address this gap by developing and validating a personalized FSI model for the fetal aorta. Our approach involved reconstructing the anatomy and flow of the fetal aorta using fetal echocardiography and ultrasound. We developed an innovative iterative method that includes: (i) an automated process for incorporating Windkessel models at outflow boundaries when clinical data is limited because of the resolution constraints of fetal imaging, (ii) an inverse approach to estimate bulk material properties, and (iii) an FSI model for high-fidelity hemodynamic evaluation. This method is efficient, typically converging in fewer than three iterations. We analyzed four normal fetal aortas with gestational ages ranging from 23.5 to 35.5 weeks to validate our workflow. We compared results with in vivo velocity waveforms across a car

Morphological analysis and identification of pathologies in the aorta are important for cardiovascular diagnosis and risk assessment in patients. Manual annotation is time-consuming and cumbersome in CT scans acquired without contrast enhancement and with low radiation dose. Hence, we propose an automatic method to segment the ascending aorta, the aortic arch and the thoracic descending aorta in low-dose chest CT without contrast enhancement. Segmentation was performed using a dilated convolutional neural network (CNN), with a receptive field of 131X131 voxels, that classified voxels in axial, coronal and sagittal image slices. To obtain a final segmentation, the obtained probabilities of the three planes were averaged per class, and voxels were subsequently assigned to the class with the highest class probability. Two-fold cross-validation experiments were performed where ten scans were used to train the network and another ten to evaluate the performance. Dice coefficients of 0.83, 0.86 and 0.88, and Average Symmetrical Surface Distances (ASSDs) of 2.44, 1.56 and 1.87 mm were obtained for the ascending aorta, the aortic arch, and the descending aorta, respectively. The results in

Automatic aorta segmentation from 3-D medical volumes is an important yet difficult task. Several factors make the problem challenging, e.g. the possibility of aortic dissection or the difficulty with segmenting and annotating the small branches. This work presents a contribution by the MedGIFT team to the SEG.A challenge organized during the MICCAI 2023 conference. We propose a fully automated algorithm based on deep encoder-decoder architecture. The main assumption behind our work is that data preprocessing and augmentation are much more important than the deep architecture, especially in low data regimes. Therefore, the solution is based on a variant of traditional convolutional U-Net. The proposed solution achieved a Dice score above 0.9 for all testing cases with the highest stability among all participants. The method scored 1st, 4th, and 3rd in terms of the clinical evaluation, quantitative results, and volumetric meshing quality, respectively. We freely release the source code, pretrained model, and provide access to the algorithm on the Grand-Challenge platform.

Cardiovascular diseases are among the most important causes of global mortality. Computational Fluid Dynamics (CFD) is a powerful research tool that analyzes the hemodynamics of artery and blood flow patterns. In this study, CFD simulations are performed to assess the patient-specific healthy aorta, fusiform, and saccular aneurysm with various mesh types, including tetrahedral, polyhedral, and poly-hexacore. The aim of this study is to explore how different mesh types and grid densities impact the hemodynamic properties of physiological flows, with the goal of identifying the most cost-effective meshing approach. A mesh independence study is carried out to ensure the precision of the results, considering the wall shear stress distribution. For this, five different mesh resolutions are generated for each geometry. The uncertainties of the simulations associated with the discretization techniques and solutions are evaluated using the Grid Convergence Index (GCI) method. The findings showed that increasing the mesh density provides smaller uncertainty. GCI values for the wall shear stress are in the range of convergence, indicating that the results are reliable and accurate. Mesh type

The current work is done to see which artery has more chance of having cardiovascular diseases by measuring value of pressure gradient in the common carotid artery (CCA) and ascending aorta according to age and gender. Pressure gradient is determined in the CCA and ascending aorta of presumed healthy volunteers, having age between 10 and 60 years. A real 2D model of both aorta and common carotid artery is constructed for different age groups using computational fluid dynamics (CFD). Pressure gradient of both the arteries are calculated and compared for different age groups and gender. It is found that with increase in diameter of common carotid artery and ascending aorta with advancing age pressure gradient decreases. The value of pressure gradient of aorta is found less than common carotid artery in both cases of age and gender.

Segmentation networks are not explicitly imposed to learn global invariants of an image, such as the shape of an object and the geometry between multiple objects, when they are trained with a standard loss function. On the other hand, incorporating such invariants into network training may help improve performance for various segmentation tasks when they are the intrinsic characteristics of the objects to be segmented. One example is segmentation of aorta and great vessels in computed tomography (CT) images where vessels are found in a particular geometry in the body due to the human anatomy and they mostly seem as round objects on a 2D CT image. This paper addresses this issue by introducing a new topology-aware loss function that penalizes topology dissimilarities between the ground truth and prediction through persistent homology. Different from the previously suggested segmentation network designs, which apply the threshold filtration on a likelihood function of the prediction map and the Betti numbers of the ground truth, this paper proposes to apply the Vietoris-Rips filtration to obtain persistence diagrams of both ground truth and prediction maps and calculate the dissimila

Medical image segmentation often requires segmenting multiple elliptical objects on a single image. This includes, among other tasks, segmenting vessels such as the aorta in axial CTA slices. In this paper, we present a general approach to improving the semantic segmentation performance of neural networks in these tasks and validate our approach on the task of aorta segmentation. We use a cascade of two neural networks, where one performs a rough segmentation based on the U-Net architecture and the other performs the final segmentation on polar image transformations of the input. Connected component analysis of the rough segmentation is used to construct the polar transformations, and predictions on multiple transformations of the same image are fused using hysteresis thresholding. We show that this method improves aorta segmentation performance without requiring complex neural network architectures. In addition, we show that our approach improves robustness and pixel-level recall while achieving segmentation performance in line with the state of the art.

Lumped method (Electrical analogy) is a quick and easy way to model human cardiovascular system. In this paper Lumped method is used for simulating a complete model. It describes a 36-vessel model and cardiac system of human body with details that could show hydrodynamic parameters of cardiovascular system. Also this paper includes modeling of pulmonary, atrium, left and right ventricles with their equivalent circuits. Exact modeling of right and left ventricles pressure with division of ascending aorta into 27 segments increases the accuracy of our simulation. In this paper we show that a calculated pressure for aorta from our complex circuit is near to measured pressure by using advanced medical instruments. Also it is shown that pressure graph from brachial is so near to aortic pressure because of this its pressure signal is usable instead of aortic pressure. Furthermore, obstruction in ascending aorta, brachial and its effects has been showed in different figures.

An experimental study is reported which investigates the wall shear stress (WSS) distribution in a transparent model of the human aorta comparing a bileaflet mechanical heart valve (BMHV) with a trileaflet mechanical heart valve (TMHV) in physiological pulsatile flow. Elastic micro-pillar WSS sensors, calibrated by micro-Particle-Image-Velocimetry measurement, are applied to the wall along the ascending aorta. Peak WSS values are observed almost twice in BMHV compared to TMHV. Flow field analyses illuminate that these peaks are linked to the jet-like flows generated in the valves interacting with the aortic wall. Not only the magnitude but also the impact regions are specific for the different valve designs. The side-orifice jets generated by BMHV travel along the aortic wall in the ascending aorta and cause a whole range impact, while the jets generated by TMHV impact further downstream in the ascending aortic generating less severe WSS.

Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoracic aortic aneurysms (ATAA). To accurately reproduce hemodynamics, computational fluid dynamics (CFD) models must employ realistic inflow boundary conditions (BCs). However, the limited availability of in vivo velocity measurements still makes researchers resort to idealized BCs. In this study we generated and thoroughly characterized a large dataset of synthetic 4D aortic velocity profiles suitable to be used as BCs for CFD simulations. 4D flow MRI scans of 30 subjects with ATAA were processed to extract cross-sectional planes along the ascending aorta, ensuring spatial alignment among all planes and interpolating all velocity fields to a reference configuration. Velocity profiles of the clinical cohort were extensively characterized by computing flow morphology descriptors of both spatial and temporal features. By exploiting principal component analysis (PCA), a statistical shape model (SSM) of 4D aortic velocity profiles was built and a dataset of 437 synthetic cases with realistic properties was generated. Comparison between clinical and synthetic datasets showed that th

Wind-forced near-inertial waves (NIWs) propagate through a sea of mesoscale eddies, which can fundamentally alter their evolution. The nature of this NIW--mesoscale interaction depends on how dispersive the waves are. For weakly dispersive waves, ray tracing suggests that the NIW frequency should be shifted by $\frac{1}{2}ζ$, where $ζ$ is the mesoscale vorticity, and that the waves are refracted into anticyclones. Strongly dispersive waves, in contrast, retain the large-scale structure of the wind forcing and exhibit a small negative frequency shift. Previous in situ observational studies have indeed revealed varying degrees of NIW--mesoscale interaction. Here, observations of NIWs from drifters are used to map the geography of NIW--mesoscale interactions globally, and idealized simulations and a simple model are used to identify the underlying physical processes. Almost everywhere in the ocean, with the notable exception of the North Pacific, the NIW frequency is strongly modulated by the mesoscale vorticity, with the slope of the frequency shift vs. vorticity taking values of approximately $0.4$. Concentration of NIW energy into anticyclones is a common feature throughout the oce

Medical imaging is an important tool for the diagnosis and the evaluation of an aortic dissection (AD); a serious condition of the aorta, which could lead to a life-threatening aortic rupture. AD patients need life-long medical monitoring of the aortic enlargement and of the disease progression, subsequent to the diagnosis of the aortic dissection. Since there is a lack of 'healthy-dissected' image pairs from medical studies, the application of inpainting techniques offers an alternative source for generating them by doing a virtual regression from dissected aortae to healthy aortae; an indirect way to study the origin of the disease. The proposed inpainting tool combines a neural network, which was trained on the task of inpainting aortic dissections, with an easy-to-use user interface. To achieve this goal, the inpainting tool has been integrated within the 3D medical image viewer of StudierFenster (www.studierfenster.at). By designing the tool as a web application, we simplify the usage of the neural network and reduce the initial learning curve.