M
Marcos Salganicoff
Researcher at Siemens
Publications - 66
Citations - 3464
Marcos Salganicoff is an academic researcher from Siemens. The author has contributed to research in topics: Segmentation & Boosting (machine learning). The author has an hindex of 23, co-authored 66 publications receiving 2754 citations. Previous affiliations of Marcos Salganicoff include University of Florida & Johns Hopkins University.
Papers
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Journal ArticleDOI
The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.
Samuel G. Armato,Geoffrey McLennan,Luc Bidaut,Michael F. McNitt-Gray,Charles R. Meyer,Anthony P. Reeves,Binsheng Zhao,Denise R. Aberle,Claudia I. Henschke,Eric A. Hoffman,Ella A. Kazerooni,Heber MacMahon,Edwin J. R. van Beek,David F. Yankelevitz,Alberto Biancardi,Peyton H. Bland,Matthew S. Brown,Roger Engelmann,Gary E. Laderach,Daniel Max,Richard C. Pais,David Qing,Rachael Y. Roberts,Amanda R. Smith,Adam Starkey,Poonam Batra,Philip Caligiuri,Ali Farooqi,Gregory W. Gladish,C. Matilda Jude,Reginald F. Munden,Iva Petkovska,Leslie E. Quint,Lawrence H. Schwartz,Baskaran Sundaram,Lori E. Dodd,Charles Fenimore,David Gur,Nicholas Petrick,John Freymann,Justin Kirby,Brian Hughes,Alessi Vande Casteele,Sangeeta Gupte,Maha Sallam,Michael D. Heath,Michael Kuhn,Ekta Dharaiya,Richard Burns,David Fryd,Marcos Salganicoff,Vikram Anand,Uri Shreter,Stephen Vastagh,Barbara Y. Croft,Laurence P. Clarke +55 more
TL;DR: The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus and is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.
Journal ArticleDOI
Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models
TL;DR: A new algorithm that is applicable to solid, non-solid and part-solid types and solitary, vascularized, and juxtapleural types is proposed that separates lung parenchyma and radiographically denser anatomical structures with coupled competition and diffusion processes.
Book ChapterDOI
Hierarchical segmentation and identification of thoracic vertebra using learning-based edge detection and coarse- to-fine deformable model
TL;DR: A new method based on learned bonestructure edge detectors and a coarse-to-fine deformable surface model is proposed to segment and identify vertebrae in 3D CT thoracic images and achieves a success rate comparable or slightly better than state-of-the-art.
Journal ArticleDOI
Accuracy of automated volumetry of pulmonary nodules across different multislice CT scanners
Marco Das,Julia Ley-Zaporozhan,Hester A. Gietema,Andre Czech,Georg Mühlenbruch,Andreas H. Mahnken,M. Katoh,Annemarie Bakai,Marcos Salganicoff,Stefan Diederich,Mathias Prokop,Hans-Ulrich Kauczor,Rolf W. Günther,Joachim E. Wildberger +13 more
TL;DR: Nodule volumetry is accurate with a reasonable volume error in data from different scanner vendors, which may have an important impact for intraindividual follow-up studies.
Proceedings ArticleDOI
Stratified learning of local anatomical context for lung nodules in CT images
TL;DR: This paper develops a fully automated voxel-by-voxel labeling/segmentation method of nodule, vessel, fissure, lung wall and parenchyma given a 3D lung image, via a unified feature set and classifier under conditional random field.