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Accurate and efficient segmentation of coronary arteries from CTA images is crucial for diagnosing and treating cardiovascular diseases. This study proposes a structured approach that combines vesselness enhancement, heart region of interest (ROI) extraction, and the ResUNet deep learning method to accurately and efficiently extract coronary artery vessels. Vesselness enhancement and heart ROI extraction significantly improve the accuracy and efficiency of the segmentation process, while ResUNet enables the model to capture both local and global features. The proposed method outperformed other state-of-the-art methods, achieving a Dice similarity coefficient (DSC) of 0.867, a Recall of 0.881, and a Precision of 0.892. The exceptional results for segmenting coronary arteries from CTA images demonstrate the potential of this method to significantly contribute to accurate diagnosis and effective treatment of cardiovascular diseases.

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Few reports study the effects of the anatomical structure of the iliac vein on hemodynamics and the methods to reduce and delay in-stent thrombosis. The anatomical structure of iliac vein stenosis was used to establish vascular models with different stenosis rates, taper angle, and left branch tilt angle in the work. The influence of anatomical structure on hemodynamics was revealed through theoretical research and in vitro experimental verification. A real iliac vein model was built based on computed tomography angiography (CTA) images, and hemorheological parameters including time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI) and relative residence time (RRT) were analyzed by computational fluid dynamics (CFD). The results showed that iliac vein stenosis could significantly increase the wall shear stress (WSS) of the blood vessels at the stenosis site and outside the intersection area, which was easy to produce eddy currents in the distal blood vessels. With the increased taper angle, the proportion of low-wall shear stress areas and the risk of thrombosis increased. A small tilt angle could aggravate the influence of narrow blood vessels on the blood flow characteristics and vascular wall. The numerical simulation results were consistent with the theoretical research results, and the experimental study verified the correctness of the simulation. The work is helpful to further understand the hemodynamic characteristics of the iliac vein, providing a scientific reference for clinical treatment and diagnosis.

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The detection and segmentation of thrombi are essential for monitoring the disease progression of abdominal aortic aneurysms (AAAs) and for patient care and management. As they have inherent capabilities to learn complex features, deep convolutional neural networks (CNNs) have been recently introduced to improve thrombus detection and segmentation. However, investigations into the use of CNN methods is in the early stages and most of the existing methods are heavily concerned with the segmentation of thrombi, which only works after they have been detected. In this work, we propose a fully automated method for the whole process of the detection and segmentation of thrombi, which is based on a well-established mask region-based convolutional neural network (Mask R-CNN) framework that we improve with optimized loss functions. The combined use of complete intersection over union (CIoU) and smooth L1 loss was designed for accurate thrombus detection and then thrombus segmentation was improved with a modified focal loss. We evaluated our method against 60 clinically approved patient studies (i.e., computed tomography angiography (CTA) image volume data) by conducting 4-fold cross-validation. The results of comparisons to multiple other state-of-the-art methods suggested the superior performance of our method, which achieved the highest F1 score for thrombus detection (0.9197) and outperformed most metrics for thrombus segmentation.

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PURPOSE: An abdominal aortic aneurysm (AAA) is known as a cardiovascular disease involving localized deformation (swelling or enlargement) of aorta occurring between the renal and iliac arteries. AAA would jeopardize patients' lives due to its rupturing risk, so prompt recognition and diagnosis of this disorder is vital. Although computed tomography angiography (CTA) is the preferred imaging modality used by radiologist for diagnosing AAA, computed tomography (CT) images can be used too. In the recent decade, there has been several methods suggested by experts in order to find a precise automated way to diagnose AAA without human intervention base on CT and CTA images. Despite great approaches in some methods, most of them need human intervention and they are not fully automated. Also, the error rate needs to decrease in other methods. Therefore, finding a novel fully automated with lower error rate algorithm using CTA and CT images for Abdominal region segmentation, AAA detection, and disease severity classification is the main goal of this paper. METHODS: The proposed method in this article will be performed in three steps: (1) designing a classifier based on Convolutional Neural Network (CNN) for classifying different parts of abdominal into four different classes such as: abdominal inside region, aorta, body border, and bone. (2) After correct aorta detection, defining its edge and measuring its diameter with the use of Hough Circle Algorithm (which is an algorithm for finding an arbitrary shape in images and measuring its diameter in pixel) is the second step. (3) Ultimately, the detected aorta, depending on its diameter, will be categorized in one of these groups: (a) there is no risk of AAA, (b) there is a medium risk of AAA, and (c) there is a high risk of AAA. RESULTS: The designed CNN classifier classifies different parts of abdominal into four different classes such as: abdominal inside region, aorta, body border, and bone with the accuracy, precision, and sensitivity of 97.93, 97.94, and 97.93% respectively. The accuracy of the proposed classifier for aorta region detection is 98.62% and Hough Circles algorithm can classify 120 aorta patches according to their diameter with accuracy of 98.33%. CONCLUSIONS: As a whole, a classifier using Convolutional Neural Network is designed and applied in order to detect AAA region among other abdominal regions. Then Hough Circles algorithm is applied to aorta patches for finding aorta border and measuring its diameter. Ultimately, the detected aortas will be categorized according to their diameters. All steps meet the expected results.

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In this paper, we will discuss and compare the stereoscopic models developed from two types of radiographic data, Magnetic Resonance Angiography (MRA) images and Computed Tomography Angiography (CTA) images. Stereoscopic models were created using surface or volume segmentation and semi-auto combined segmentation techniques. Although, the CTA data were found to improve the speed and quality of constructing virtual vascular models compared to conventional CT data, small blood vessels were difficult to capture during the imaging and reconstruction process thereby limiting the fidelity of the stereoscopic models. Thus, high contrast Magnetic Resonance Angiography (MRA) images offer better resolution to visualize and capture the smaller branches of the cerebral vasculature than CTA images.

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