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Annemarieke Rutten
Researcher at Utrecht University
Publications - 30
Citations - 1331
Annemarieke Rutten is an academic researcher from Utrecht University. The author has contributed to research in topics: Agatston score & Coronary arteries. The author has an hindex of 16, co-authored 30 publications receiving 1205 citations. Previous affiliations of Annemarieke Rutten include University Medical Center Utrecht.
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Journal ArticleDOI
Multi-Atlas-Based Segmentation With Local Decision Fusion—Application to Cardiac and Aortic Segmentation in CT Scans
Ivana Išgum,Marius Staring,Annemarieke Rutten,Mathias Prokop,Max A. Viergever,B. van Ginneken +5 more
TL;DR: The proposed method outperforms other methods and yields results very close to those of an independent human observer, especially on the segmentation of the heart and the aorta in computed tomography scans of the thorax.
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Peri-coronary epicardial adipose tissue is related to cardiovascular risk factors and coronary artery calcification in post-menopausal women
Alexander M. de Vos,Mathias Prokop,Cornelis J. Roos,Matthijs F.L. Meijs,Yvonne T. van der Schouw,Annemarieke Rutten,Petra M. Gorter,Maarten-Jan M. Cramer,Pieter A. Doevendans,Benno J. Rensing,Marie-Louise Bartelink,B. K. Velthuis,A. Mosterd,Michiel L. Bots +13 more
TL;DR: The hypothesis that peri-coronary epicardial adipose tissue affects coronary atherosclerosis and possibly coronary risk is supported, and EAT is strongly related to vascular risk factors and coronary calcification.
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Noise Reduction in Computed Tomography Scans Using 3-D Anisotropic Hybrid Diffusion With Continuous Switch
TL;DR: Noise filtering techniques that maintain image contrast while decreasing image noise have the potential to optimize the quality of computed tomography (CT) images acquired at reduced radiation dose, and a hybrid diffusion filter with continuous switch (HDCS) is introduced.
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Detection of coronary calcifications from computed tomography scans for automated risk assessment of coronary artery disease.
TL;DR: A fully automated method for coronary calcification detection from non-contrast-enhanced, ECG-gated multi-slice computed tomography (CT) data is presented and the best performance was obtained employing a two-stage classification system with a k-nearest neighbor (k-NN) classifier and a feature selection scheme.
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Coronary calcification: effect of small variation of scan starting position on Agatston, volume, and mass scores.
TL;DR: A small variation in scan starting position can substantially influence calcium measurements and poses an inherent limit to calcium scoring with contiguous 3-mm sections.