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

• Published in: NeuroImage (Orlando, Fla.) Vol. 170; pp. 482 - 494
• Main Authors: Souza, Roberto, Lucena, Oeslle, Garrafa, Julia, Gobbi, David, Saluzzi, Marina, Appenzeller, Simone, Rittner, Letícia, Frayne, Richard, Lotufo, Roberto
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.04.2018; Elsevier Limited
• Subjects: Adult; Aged; Aged, 80 and over; Algorithms; Automation; Brain - diagnostic imaging; Brain extraction; Brain MR image analysis; Brain research; Brain segmentation; Consensus; Datasets; Datasets as Topic; Female; Humans; Image processing; Image Processing, Computer-Assisted - methods; Learning algorithms; Machine Learning; Magnetic Fields; Magnetic Resonance Imaging - methods; Male; Middle Aged; MP-RAGE; Neural networks; Neuroimaging; Neuroimaging - methods; Open access; Public database; Scanners; Segmentation; Skull; Skull - diagnostic imaging; Skull stripping; Software; Spinal cord; Statistical analysis; Canada; Calgary Alberta Canada; Public database; Brain segmentation; Skull stripping; Brain MR image analysis; MP-RAGE; Brain extraction
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2017.08.021

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. •A public multi-vendor, multi-field-strength brain MR dataset is proposed and it is now available for download at http://miclab.fee.unicamp.br/tools.•Consensus masks are used as “silver-standards” to assess agreement between different skull stripping methods.•Influences of scanner magnetic field strength and scanner vendor on skull stripping results are analyzed.