1 citations · 1 across the 5 of their papers we have counts for
6 papers
ImageCAS-X: a dataset and benchmark for coronary artery segmentation and centerline extraction in coronary CT angiography
Kit M. Bransby, Esther Øksnebjerg, Kristoffer Kjær +7
Accurate segmentation of the coronary vessel lumen is a prerequisite for quantitative assessment of atherosclerotic plaque and perivascular adipose tissue in coronary computed tomo…
Anatomy-Aware 3D Mesh Refinement of Pericardium Segmentations on Computed Tomography
Andreas W. Aspe, Jonas Jalili Loft, Michael Huy Cuong Pham +6
Accurate delineation of the pericardium in a cardiac CT scan is essential for quantifying epicardial adipose tissue, yet it remains one of the most challenging structures to segmen…
Automated Cardiac Adipose Tissue Segmentation in Computed Tomography: A Literature Review
Andreas W. Aspe, Jonas Jalili Pedersen, Andreas Ohrt Johansen +4
This review provides an overview of recent advancements in automated segmentation methods on Computed Tomography (CT) for two types of cardiac fat: Epicardial adipose Tissue (EAT)…
A public cardiac CT dataset featuring the left atrial appendage
Bjoern Hansen, Jonas Pedersen, Klaus F. Kofoed +3
Despite the success of advanced segmentation frameworks such as TotalSegmentator (TS), accurate segmentations of the left atrial appendage (LAA), coronary arteries (CAs), and pulmo…
Implicit Neural Representations for Registration of Left Ventricle Myocardium During a Cardiac Cycle
Mathias Micheelsen Lowes, Jonas Jalili Pedersen, Bjørn S. Hansen +3
Understanding the movement of the left ventricle myocardium (LVmyo) during the cardiac cycle is essential for assessing cardiac function. One way to model this movement is through…
Spatio-temporal neural distance fields for conditional generative modeling of the heart
Kristine Sørensen, Paula Diez, Jan Margeta +8
The rhythmic pumping motion of the heart stands as a cornerstone in life, as it circulates blood to the entire human body through a series of carefully timed contractions of the in…