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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

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

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Autor(es):
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]
Número total de Autores: 9
Afiliação do(s) autor(es):
[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
Número total de Afiliações: 6
Tipo de documento: Artigo de Revisão
Fonte: NeuroImage; v. 170, n. SI, p. 482-494, APR 15 2018.
Citações Web of Science: 3
Resumo

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)

Processo FAPESP: 13/07559-3 - Instituto Brasileiro de Neurociência e Neurotecnologia - BRAINN
Beneficiário:Fernando Cendes
Linha de fomento: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
Processo FAPESP: 16/18332-8 - Segmentação de estruturas cerebrais de imagens de ressonância magnética utilizando deep learning
Beneficiário:Oeslle Alexandre Soares de Lucena
Linha de fomento: Bolsas no Brasil - Mestrado
Processo FAPESP: 13/23514-0 - Árvore máxima: teoria, algoritmos e aplicações
Beneficiário:Roberto Medeiros de Souza
Linha de fomento: Bolsas no Brasil - Doutorado