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
Robust 3D Segmentation of Pulmonary Nodules in Multislice CT Images
TL;DR: A robust and accurate algorithm for segmenting the 3D pulmonary nodules in multislice CT scans that reliably segments a variety of nodules including part- or non-solid nodules which poses difficulty for the existing solutions.
Patent
Data Transmission in Remote Computer Assisted Detection
Michal Sofka,Kristof Ralovich,Jingdan Zhang,Shaohua Kevin Zhou,Gianluca Paladini,Dorin Comaniciu +5 more
TL;DR: In this paper, a hierarchal detection is used, allowing detection on data at progressively greater resolutions, by limiting the number and/or size of regions provided at higher resolutions based on the previous detection.
Patent
Method and system for polyp segmentation for 3d computed tomography colonography
Le Lu,Adrian Barbu,Matthias Wolf,Sarang Lakare,Luca Bogoni,Marcos Salganicoff,Dorin Comaniciu +6 more
TL;DR: In this article, a method and system for polyp segmentation in computed tomography colonogrphy (CTC) volumes is disclosed, which utilizes a three-staged probabilistic binary classification approach for automatically segmenting polyp voxels from surrounding tissue in CTC volumes.
Journal ArticleDOI
Quantifying and leveraging predictive uncertainty for medical image assessment
Florin C. Ghesu,Bogdan Georgescu,Awais Mansoor,Youngjin Yoo,Eli Gibson,R.S. Vishwanath,Abishek Balachandran,James M. Balter,Yue Cao,Ramandeep Singh,Subba R. Digumarthy,Mannudeep K. Kalra,Sasa Grbic,Dorin Comaniciu +13 more
TL;DR: In this article, the authors proposed a system that learns not only the probabilistic estimate for classification, but also an explicit uncertainty measure which captures the confidence of the system in the predicted output.
Posted Content
Decompose to manipulate: Manipulable Object Synthesis in 3D Medical Images with Structured Image Decomposition.
Siqi Liu,Eli Gibson,Sasa Grbic,Zhoubing Xu,Arnaud Arindra Adiyoso Setio,Jie Yang,Bogdan Georgescu,Dorin Comaniciu +7 more
TL;DR: A framework for synthesizing 3D objects, such as pulmonary nodules, in 3D medical images with manipulable properties is proposed and it is shown the synthetic patches could improve the overall nodule detection performance by average 8.44% competition performance metric (CPM) score.