Related papers: Segmentation and Vascular Vectorization for Corona…
We present a cascade deep neural network to segment retinal vessels in volumetric optical coherence tomography (OCT). Two types of knowledge are infused into the network for confining the searching regions. (1) Histology. The retinal…
Many studies regarding the vasculature of biological tissues involve the segmentation of the blood vessels in a sample followed by the creation of a graph structure to model the vasculature. The graph is then used to extract relevant…
Diagnosis of cardiovascular disease using automated methods often relies on the critical task of cardiac image segmentation. We propose a novel strategy that performs segmentation using specialist networks that focus on a single anatomy…
Digital subtraction angiography (DSA) in coronary imaging is fundamentally challenged by physiological motion, forcing reliance on raw angiograms cluttered with anatomical noise. Existing deep learning methods often produced images with two…
Segmentation of cardiac structures is one of the fundamental steps to estimate volumetric indices of the heart. This step is still performed semi-automatically in clinical routine, and is thus prone to inter- and intra-observer variability.…
Purpose: Interpreting chest radiographs (CXR) remains challenging due to the ambiguity of overlapping structures such as the lungs, heart, and bones. To address this issue, we propose a novel method for extracting fine-grained anatomical…
Pulmonary vessel segmentation is important for clinical diagnosis of pulmonary diseases, while is also challenging due to the complicated structure. In this work, we present an effective framework and refinement process of pulmonary vessel…
Quantification of cardiac structures in non-contrast CT (NCCT) could improve cardiovascular risk stratification. However, setting a manual reference to train a fully convolutional network (FCN) for automatic segmentation of NCCT images is…
We present a fully automated method for top-down segmentation of the pulmonary arterial tree in low-dose thoracic CT images. The main basal pulmonary arteries are identified near the lung hilum by searching for candidate vessels adjacent to…
Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In this paper, we propose a novel deep learning method for joint estimation of motion and…
Image translation across domains for unpaired datasets has gained interest and great improvement lately. In medical imaging, there are multiple imaging modalities, with very different characteristics. Our goal is to use cross-modality…
Accurate segmentation of carotid artery structures in histopathological images is vital for cardiovascular disease research. This study systematically evaluates ten deep learning segmentation models including classical architectures, modern…
An abdominal aortic aneurysm (AAA) is a focal dilation of the aorta that, if not treated, tends to grow and may rupture. A significant unmet need in the assessment of AAA disease, for the diagnosis, prognosis and follow-up, is the…
Due to the lack of automated methods, to diagnose cerebrovascular disease, time-of-flight magnetic resonance angiography (TOF-MRA) is assessed visually, making it time-consuming. The commonly used encoder-decoder architectures for…
Purpose of Review Recently, machine learning has developed rapidly in the field of medicine, playing an important role in disease diagnosis. Our aim of this paper is to provide an overview of the advancements in machine learning techniques…
Automated segmentation of Cardiac Magnetic Resonance (CMR) plays a pivotal role in efficiently assessing cardiac function, offering rapid clinical evaluations that benefit both healthcare practitioners and patients. While recent research…
Intracoronary Optical Coherence Tomography (OCT) enables high-resolution visualization of coronary vessel anatomy but presents challenges due to noise, imaging artifacts, and complex tissue structures. This paper proposes a fully automated…
Generating computational anatomical models of cerebrovascular networks is vital for improving clinical practice and understanding brain oxygen transport. This is achieved by extracting graph-based representations based on pre-mapping of…
Human veins are important for carrying the blood from the body-parts to the heart. The improper functioning of the human veins may arise from several venous diseases. Varicose vein is one such disease wherein back flow of blood can occur,…
Medical image segmentation is one of the important tasks of computer-aided diagnosis in medical image analysis. Since most medical images have the characteristics of blurred boundaries and uneven intensity distribution, through existing…