D
Dorin Comaniciu
Researcher at Princeton University
Publications - 632
Citations - 43059
Dorin Comaniciu is an academic researcher from Princeton University. The author has contributed to research in topics: Segmentation & Object detection. The author has an hindex of 74, co-authored 622 publications receiving 40541 citations. Previous affiliations of Dorin Comaniciu include Siemens & Rutgers University.
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Patent
Image-based tumor phenotyping with machine learning from synthetic data
TL;DR: In this article, a computational tumor model is used to create synthetic medical images for image-based tumor phenotyping in a medical system and a machine-trained classifier is applied to images from actual patients to predict tumor phenotype for that patient based on the knowledge learned from the synthetic images.
Posted Content
Machine Learning Automatically Detects COVID-19 using Chest CTs in a Large Multicenter Cohort
Eduardo J. Mortani Barbosa,Bogdan Georgescu,Shikha Chaganti,Gorka Bastarrika Alemañ,Jordi Broncano Cabrero,Guillaume Chabin,Thomas Flohr,Philippe Grenier,Sasa Grbic,Nakul Gupta,François Mellot,Savvas Nicolaou,Thomas Re,Pina C. Sanelli,Alexander W. Sauter,Youngjin Yoo,Valentin Ziebandt,Dorin Comaniciu +17 more
TL;DR: This new method accurately discriminates COVID-19 from other types of pneumonia, ILD, and CTs with no pathologies, using quantitative imaging features derived from chest CT, while balancing interpretability of results and classification performance and, therefore, may be useful to facilitate diagnosis of CO VID-19.
Patent
System and Method for Detecting Landmarks in a Three-Dimensional Image Volume
TL;DR: In this paper, a method and apparatus for detecting vascular landmarks in a 3D image volume, such as a CT volume, was disclosed, where one or more guide slices are detected in 3D images and a set of landmark candidates for multiple target vascular landmarks are then detected based on the guide slices.
Book ChapterDOI
Personalized Pulmonary Trunk Modeling for Intervention Planning and Valve Assessment Estimated from CT Data
Dime Vitanovski,Razvan Ioan Ionasec,Bogdan Georgescu,Martin Huber,Andrew M. Taylor,Joachim Hornegger,Dorin Comaniciu +6 more
TL;DR: In this paper, a hierarchical estimation based on robust learning methods is applied to identify the patient-specific model parameters from volumetric CT scans, which involves detection of piecewise affine parameters, fast centreline computation and local surface delineation.
Patent
Method and system for precise segmentation of the left atrium in C-arm computed tomography volumes
TL;DR: In this article, a method and system for multi-part left atrium segmentation in a C-arm CT volume is described, where multiple LA part models, including an LA chamber body mesh, an appendage mesh, a left inferior pulmonary vein (PV) mesh and a left superior PV mesh, are segmented in a 3D volume.