title: Mindboggling morphometry of human brains.
abstract: Mindboggle (http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans. The number of different shape measures and the size of the populations make this the largest and most detailed shape analysis of human brains ever conducted. Brain image morphometry shows great potential for providing much-needed biological markers for diagnosing, tracking, and predicting progression of mental health disorders. Very few software algorithms provide more than measures of volume and cortical thickness, while more subtle shape measures may provide more sensitive and specific biomarkers. Mindboggle computes a variety of (primarily surface-based) shapes: area, volume, thickness, curvature, depth, Laplace-Beltrami spectra, Zernike moments, etc. We evaluate Mindboggle's algorithms using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist. All data, code, and results of these evaluations are publicly available.
authors.affiliation: Child Mind Institute, New York, New York, United States of America.
authors.forename: Arno
authors.identifier: 0000-0002-0707-2889
authors.initials: A
authors.lastname: Klein
authors.affiliation: McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.|Department of Otolaryngology, Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: Satrajit S
authors.identifier: 0000-0002-5312-6729
authors.initials: SS
authors.lastname: Ghosh
authors.affiliation: Department of Electrical and Computer Engineering, University of Akron, Akron, Ohio, United States of America.
authors.forename: Forrest S
authors.initials: FS
authors.lastname: Bao
authors.affiliation: University of Louvain, Louvain, Belgium.
authors.forename: Joachim
authors.initials: J
authors.lastname: Giard
authors.affiliation: Columbia University, New York, New York, United States of America.
authors.forename: Yrjö
authors.identifier: 0000-0002-0698-0295
authors.initials: Y
authors.lastname: Häme
authors.affiliation: Columbia University, New York, New York, United States of America.
authors.forename: Eliezer
authors.initials: E
authors.lastname: Stavsky
authors.affiliation: Columbia University, New York, New York, United States of America.
authors.forename: Noah
authors.initials: N
authors.lastname: Lee
authors.affiliation: TankThink Labs, Boston, Massachusetts, United States of America.
authors.forename: Brian
authors.initials: B
authors.lastname: Rossa
authors.affiliation: Harvard Medical School, Cambridge, Massachusetts, United States of America.
authors.forename: Martin
authors.initials: M
authors.lastname: Reuter
authors.affiliation: Sage Bionetworks, Seattle, Washington, United States of America.
authors.forename: Elias
authors.initials: E
authors.lastname: Chaibub Neto
authors.affiliation: University of California San Francisco, San Francisco, California, United States of America.
authors.forename: Anisha
authors.identifier: 0000-0003-3554-043X
authors.initials: A
authors.lastname: Keshavan
mesh_terms: D000465:Algorithms; D059925:Anatomic Landmarks / Q000000981:diagnostic imaging*; D001921:Brain / Q000473:pathology*; D001927:Brain Diseases / Q000000981:diagnostic imaging* / Q000473:pathology*; D038524:Diffusion Magnetic Resonance Imaging; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008297:Male; D009929:Organ Size; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D013382:Subtraction Technique