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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement

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Souza, Roberto [1, 2, 3, 4, 5] ; Lucena, Oeslle [4] ; Garrafa, Julia [6] ; Gobbi, David [1, 2, 3] ; Saluzzi, Marina [1, 2, 3] ; Appenzeller, Simone [6] ; Rittner, Leticia [4] ; Frayne, Richard [1, 2, 3, 5] ; Lotufo, Roberto [4]
Total Authors: 9
[1] Univ Calgary, Hotchkiss Brain Inst, Dept Radiol, Calgary, AB - Canada
[2] Univ Calgary, Hotchkiss Brain Inst, Dept Clin Neurosci, Calgary, AB - Canada
[3] Alberta Hlth Serv, Foothills Med Ctr, Calgary Image Proc & Anal Ctr, Calgary, AB - Canada
[4] Univ Estadual Campinas, Dept Comp Engn & Ind Automat, Med Imaging & Comp Lab, Campinas, SP - Brazil
[5] Alberta Hlth Serv, Foothills Med Ctr, Seaman Family Magnet Resonance Res Ctr, Calgary, AB - Canada
[6] Univ Estadual Campinas, Fac Med Sci, Div Rheumatol, Campinas, SP - Brazil
Total Affiliations: 6
Document type: Review article
Source: NeuroImage; v. 170, n. SI, p. 482-494, APR 15 2018.
Web of Science Citations: 8

This paper presents an open, multi-vendor, multi-field strength magnetic resonance (MR) T1-weighted volumetric brain imaging dataset, named Calgary-Campinas-359 (CC-359). The dataset is composed of images of older healthy adults (29-80 years) acquired on scanners from three vendors (Siemens, Philips and General Electric) at both 1.5 T and 3 T. CC-359 is comprised of 359 datasets, approximately 60 subjects per vendor and magnetic field strength. The dataset is approximately age and gender balanced, subject to the constraints of the available images. It provides consensus brain extraction masks for all volumes generated using supervised classification. Manual segmentation results for twelve randomly selected subjects performed by an expert are also provided. The CC-359 dataset allows investigation of 1) the influences of both vendor and magnetic field strength on quantitative analysis of brain MR; 2) parameter optimization for automatic segmentation methods; and potentially 3) machine learning classifiers with big data, specifically those based on deep learning methods, as these approaches require a large amount of data. To illustrate the utility of this dataset, we compared to the results of a supervised classifier, the results of eight publicly available skull stripping methods and one publicly available consensus algorithm. A linear mixed effects model analysis indicated that vendor (p - value < 0.001) and magnetic field strength (p - value < 0.001) have statistically significant impacts on skull stripping results. (C) 2017 Elsevier Inc. All rights reserved. (AU)

FAPESP's process: 13/07559-3 - BRAINN - The Brazilian Institute of Neuroscience and Neurotechnology
Grantee:Fernando Cendes
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 16/18332-8 - Deep learning for brain structures segmentation in MR imaging
Grantee:Oeslle Alexandre Soares de Lucena
Support type: Scholarships in Brazil - Master
FAPESP's process: 13/23514-0 - Max-Tree: theory, algorithms and applications
Grantee:Roberto Medeiros de Souza
Support type: Scholarships in Brazil - Doctorate