Siemens Aktiengesellschaft

Siemens Healthcare Gmbh

Rutgers, the State University of New Jersey

Sushil Mittal

Dorin Comaniciu

Bogdan Georgescu

Yefeng Zheng

Fernando Vega-Higuera

Vinay Damodar Shet

Shaohua Kevin Zhou

Andreas Hutter

Maneesh Kumar Singh

Michael Kelm

Dominik Bernhardt

Bernhard Agthe

Alexey Tsymbal

Xianjun Sam Zheng

Xiang Gao

Cheng-Hao Kuo

Peter Meer

Mayank Rana

A method and system for automatic coronary stenosis detection in computed tomography (CT) data is disclosed. Coronary artery centerlines are obtained in an input cardiac CT volume. A trained classifier, such as a probabilistic boosting tree (PBT) classifier, is used to detect stenosis regions along the centerlines in the input cardiac CT volume. The classifier classifies each of the control points that define the coronary artery centerlines as a stenosis point or a non-stenosis point.

Method and system for automatic detection of coronary stenosis in cardiac computed tomography data

Multiple cameras are configured for use in video analytics. A single configuration tool is provided. The interrelationships between cameras are included within the configuration. Using a combination of text entry fields, registration of the cameras on a floor or other map, and marking on images from the cameras, an efficient workflow for configuration may be provided.

Configuration tool for video analytics

A method for tracking pedestrians in a video sequence, where each image frame of the video sequence corresponds to a time step, includes using marginal space learning to sample a prior probability distribution p(x|Z) of multi-person identity assignments given a set of feature measurements from all previous image frames, using marginal space learning to estimate an observation likelihood distribution p(z|x) of the set of features given a set of multi-person identity assignments sampled from the prior probability distribution, calculating a posterior probability distribution p(x|Z) from the observation likelihood distribution p(z|x) and the prior probability distribution p(x|Z), and using marginal space learning to estimate the prior probability distribution p(x|Z) for a next image frame given the posterior probability distribution p(x|Z) and a probability p(x|x), where the posterior probability distribution of multi-person identity assignments corresponds to a set of pedestrian detection hypotheses for the video sequence.

Marginal space learning for multi-person tracking over mega pixel imagery

A method and system for providing detecting and classifying coronary stenoses in 3D CT image data is disclosed. Centerlines of coronary vessels are extracted from the CT image data. Non-vessel regions are detected and removed from the coronary vessel centerlines. The cross-section area of the lumen is estimated based on the coronary vessel centerlines using a trained regression function. Stenosis candidates are detected in the coronary vessels based on the estimated lumen cross-section area, and the significant stenosis candidates are automatically classified as calcified, non-calcified, or mixed.

Method and system for automatic detection and classification of coronary stenoses in cardiac CT volumes

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Highland Park, NJ, United States of America

Sushil Mittal