IEEE transactions on ultrasonics, ferroelectrics, and frequency control
32746163
Studies of medical flow imaging have technical limitations for accurate analysis of blood flow dynamics and vessel wall interaction at arteries. We propose a new deep learning-based boundary detection and compensation (DL-BDC) technique in ultrasound...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
33018492
The scope of this paper is to present a new carotid vessel segmentation algorithm implementing the U-net based convolutional neural network architecture. With carotid atherosclerosis being the major cause of stroke in Europe, new methods that can pro...
Carotid plaque segmentation in ultrasound longitudinal B-mode images using deep learning is presented in this work. We report on 101 severely stenotic carotid plaque patients. A standard U-Net is compared with a dilated U-Net architecture in which th...
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
32746180
Despite being used clinically as a noninvasive flow visualization tool, color flow imaging (CFI) is known to be prone to aliasing artifacts that arise due to fast blood flow beyond the detectable limit. From a visualization standpoint, these aliasing...
The international journal of cardiovascular imaging
33423132
Visual or manual characterization and classification of atherosclerotic plaque lesions are tedious, error-prone, and time-consuming. The purpose of this study is to develop and design an automated carotid plaque characterization and classification sy...
BACKGROUND: Non-invasive imaging is of interest for tracking the progression of atherosclerosis in the carotid bifurcation, and segmenting this region into its constituent branch arteries is necessary for analyses. The purpose of this study was to va...
Radiomics, quantitative feature extraction from radiological images, can improve disease diagnosis and prognostication. However, radiomic features are susceptible to image acquisition and segmentation variability. Ideally, only features robust to the...
INTRODUCTION: This study aimed to describe the rates and causes of unplanned readmissions within 30 days following carotid artery stenting (CAS) and to use artificial intelligence machine learning analysis for creating a prediction model for short-te...
IEEE journal of biomedical and health informatics
33600328
Measurement of total-plaque-area (TPA) is important for determining long term risk for stroke and monitoring carotid plaque progression. Since delineation of carotid plaques is required, a deep learning method can provide automatic plaque segmentatio...
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
34143734
Accurate 3-D geometries of arteries and veins are important clinical data for diagnosis of arterial disease and intervention planning. Automatic segmentation of vessels in the transverse view suffers from the low lateral resolution and contrast. Conv...