相关论文: A public cardiac CT dataset featuring the left atr…
Automatic labeling of coronary arteries is an essential task in the practical diagnosis process of cardiovascular diseases. For experienced radiologists, the anatomically predetermined connections are important for labeling the artery…
Accurate and robust whole heart substructure segmentation is crucial in developing clinical applications, such as computer-aided diagnosis and computer-aided surgery. However, segmentation of different heart substructures is challenging…
Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of…
Vessel stenosis is a major risk factor in cardiovascular diseases (CVD). To analyze the degree of vessel stenosis for supporting the treatment management, extraction of coronary artery area from Computed Tomographic Angiography (CTA) is…
Automated segmentation of human cardiac magnetic resonance datasets has been steadily improving during recent years. However, these methods are not directly applicable in preclinical context due to limited datasets and lower image…
There is a dire need for medical imaging datasets with accompanying annotations to perform downstream patient analysis. However, it is difficult to manually generate these annotations, due to the time-consuming nature, and the variability…
Accurate delineation of the left ventricle (LV) is an important step in evaluation of cardiac function. In this paper, we present an automatic method for segmentation of the LV in cardiac CT angiography (CCTA) scans. Segmentation is…
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…
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.…
Coronary artery stenosis is a critical health risk, and its precise identification in Coronary Angiography (CAG) can significantly aid medical practitioners in accurately evaluating the severity of a patient's condition. The complexity of…
The segmentation of coronary arteries by convolutional neural network is promising yet requires a large amount of labor-intensive manual annotations. Transferring knowledge from retinal vessels in widely-available public labeled fundus…
Coronary artery disease (CAD) remains a prevalent cardiovascular condition, posing significant health risks worldwide. This pathology, characterized by plaque accumulation in coronary artery walls, leads to myocardial ischemia and various…
One of the main sources of error in multi-atlas segmentation propagation approaches comes from the use of atlas databases that are morphologically dissimilar to the target image. In this work, we exploit the segmentation errors associated…
Cardiac segmentation is in great demand for clinical practice. Due to the enormous labor of manual delineation, unsupervised segmentation is desired. The ill-posed optimization problem of this task is inherently challenging, requiring…
Cardiac magnetic resonance imaging improves on diagnosis of cardiovascular diseases by providing images at high spatiotemporal resolution. Manual evaluation of these time-series, however, is expensive and prone to biased and…
Radiological imaging offers effective measurement of anatomy, which is useful in disease diagnosis and assessment. Previous study has shown that the left atrial wall remodeling can provide information to predict treatment outcome in atrial…
Coronary artery calcification (CAC) is a strong predictor of cardiovascular risk but remains underutilized in clinical routine thoracic imaging due to the need for dedicated imaging protocols and manual annotation. We present DeepCAC2, a…
In this work, we attempt the segmentation of cardiac structures in late gadolinium-enhanced (LGE) magnetic resonance images (MRI) using only minimal supervision in a two-step approach. In the first step, we register a small set of five LGE…
Accurate segmentation of heart structures imaged by cardiac MR is key for the quantitative analysis of pathology. High-resolution 3D MR sequences enable whole-heart structural imaging but are time-consuming, expensive to acquire and they…
Deep learning-based whole-heart segmentation in coronary CT angiography (CCTA) allows the extraction of quantitative imaging measures for cardiovascular risk prediction. Automatic extraction of these measures in patients undergoing only…