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Marius Staring

Researcher at Leiden University Medical Center

Publications -  132
Citations -  10612

Marius Staring is an academic researcher from Leiden University Medical Center. The author has contributed to research in topics: Image registration & Computer science. The author has an hindex of 32, co-authored 117 publications receiving 8672 citations. Previous affiliations of Marius Staring include Utrecht University & Loyola University Medical Center.

Papers
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Journal ArticleDOI

Evaluation of automated statistical shape model based knee kinematics from biplane fluoroscopy.

TL;DR: The ability of accurately fitting the edges in the fluoroscopic sequences has a larger role in determining the kinematic precision than that of the overall 3D shape accuracy, which suggests that a better shape reconstruction accuracy does not automatically imply a better kinematics precision.
Journal ArticleDOI

Pulmonary Fissure Detection in CT Images Using a Derivative of Stick Filter

TL;DR: The proposed derivative of stick (DoS) filter for fissure enhancement and a post-processing pipeline for subsequent segmentation obtained a high segmentation accuracy and was verified by visual inspection and demonstration on abnormal and pathological cases, where typical deformations were robustly detected together with normal fissures.
Book ChapterDOI

Accuracy Estimation for Medical Image Registration Using Regression Forests

TL;DR: A method was proposed that for the first time shows the feasibility of automatic registration assessment by means of regression, and promising results were obtained.
Journal ArticleDOI

Detection of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Longitudinal Brain MRI.

TL;DR: A new method to distinguish between MCI patients that either convert to Alzheimer's Disease (MCIc) or remain stable (MCIs), using only longitudinal T1-weighted MRI, which outperforms two alternative approaches that either depends on the baseline image only, or uses longitudinal information from larger brain areas.
Book ChapterDOI

Linking Convolutional Neural Networks with Graph Convolutional Networks: Application in Pulmonary Artery-Vein Separation

TL;DR: The proposed CNN-GCN method combines local image information with graph connectivity information, improving pulmonary A/V separation over a baseline CNN method, approaching the performance of human observers.