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.
Papers
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Book ChapterDOI
Data-Driven breast decompression and lesion mapping from digital breast tomosynthesis
TL;DR: This paper proposes a novel completely data-driven approach to breast shape prediction that does not necessitate prior knowledge about biomechanical properties and parameters of the breast tissue and employs machine learning methods to predict the shape of the uncompressed breast from a DBT input volume.
Book ChapterDOI
Marginal Space Learning
Yefeng Zheng,Dorin Comaniciu +1 more
TL;DR: This chapter presents an efficient 3D learning-based object detection method, called Marginal Space Learning, and introduces the steerable features, as a mechanism to search the orientation space, thus avoiding expensive volume/image rotations.
Book ChapterDOI
Segmentation based features for lymph node detection from 3-D chest CT
TL;DR: The graph cuts method is adapted to the problem of lymph nodes segmentation with a setting that requires only a single positive seed and at the same time solves the small cut problem of graph cuts.
Book ChapterDOI
A note on future research in segmentation techniques applied to neurology, cardiology, mammography and pathology
Jasit S. Suri,Sameer Singh,Seyed Kamaledin Setarehdan,Rakesh Sharma,Keir Bovis,Dorin Comaniciu,Laura Reden +6 more
TL;DR: This chapter presents the future aspects of the segmentation techniques covered in this book, and describes many different kinds of models of CVGIP1 and PR2.
Book ChapterDOI
Propagation of Myocardial Fibre Architecture Uncertainty on Electromechanical Model Parameter Estimation: A Case Study
Roch Molléro,Dominik Neumann,Marc Michel Rohé,Manasi Datar,Herve Lombaert,Nicholas Ayache,Dorin Comaniciu,Olivier Ecabert,Marcello Chinali,Gabriele Rinelli,Xavier Pennec,Maxime Sermesant,Tommaso Mansi +12 more
TL;DR: A new pipeline is presented to evaluate the impact of fibre uncertainty on the personalisation of an electromechanical model of the heart from ECG and medical images and how the uncertainty generated by this variability impacts the following personalisation.