Mindboggle 2: Automated human brain MRI feature extraction, identification, shape analysis, and labeling

Abstract

Event Abstract Back to Event Mindboggle 2: Automated human brain MRI feature extraction, identification, shape analysis, and labeling Arno Klein1*, Forrest Bao2, Eliezer Stavsky1, Yrjö Häme1, Joachim Giard3, Nolan Nichols4 and Satrajit Ghosh5 1 Columbia University, United States 2 Texas Tech University, United States 3 Universite Catholique de Louvain, Belgium 4 Washington University, United States 5 MIT, United States Mindboggle 2 is a new neuroinformatics platform that (1) extracts multiple, nested features from depth and curvature maps of a cortical surface, uses label propagation to (2) segment and identify the features and (3) label the cortical surface in areas between these features, and (4) quantifies the shapes of the identified features and labeled regions. Mindboggle is open source, Python software (http://www.mindboggle.info). We are currently applying Mindboggle in morphometry studies and region-based functional and diffusion MRI analyses. Features Mindboggle uses the Nipype pipeline framework to provide a flexible and modular way to include multiple methods for extracting features, including sulcus folds, pits (bottommost points), fundi (curves along the depths of folds), and medial surfaces ("midlines" within folds). For example, one of our pit extraction algorithms assigns a likelihood value to each point based on its depth and local surface curvature, and employs a hidden Markov measure field model to discourage spatially clustered configurations of pit points. We use a similar approach for one of our fundus extraction algorithms, where the probabilistic model is formulated to encourage elongated, connected structures that reach the full length of folds. Our medial surfaces "grow" from our fundi at the depths of a sulcus and are guided upward by vertices belonging to opposite sulcal banks. To identify features, we segment them by distinct pairs of surrounding anatomical labels. We first register multiple, manually labeled brains to a target brain, then propagate labels along the resulting probabilistic label map from consensus labels to the features. For shape analysis, we compute geometric and spectral shape measures for each feature, and sulcus spans using the normals to each medial surface point. Labels The above fundi provide a much more consistent means of defining some label boundaries than a human would be capable of. We enforce a closer correspondence between label boundaries and fundi by creating a "fundus friendly" version of the protocol by aggregating regions whose divisions are not defined by fundi, and by post-processing labeled surfaces to conform to the protocol. The label propagation used to identify fundi also automates labeling of the areas between the fundi. The result is therefore a feature-defined, fundus-friendly labeling protocol and an automated means to apply this protocol by moving label boundaries to coincide with fundi. Keywords: General neuroinformatics, neuroinformatics platform, Cortical surface area, open source, Software Development Conference: 5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012. Presentation Type: Demo Topic: Neuroinformatics Citation: Klein A, Bao F, Stavsky E, Häme Y, Giard J, Nichols N and Ghosh S (2014). Mindboggle 2: Automated human brain MRI feature extraction, identification, shape analysis, and labeling. Front. Neuroinform. Conference Abstract: 5th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2014.08.00087 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 Mar 2013; Published Online: 27 Feb 2014. * Correspondence: Dr. Arno Klein, Columbia University, New York, United States, binarybottle@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Arno Klein Forrest Bao Eliezer Stavsky Yrjö Häme Joachim Giard Nolan Nichols Satrajit Ghosh Google Arno Klein Forrest Bao Eliezer Stavsky Yrjö Häme Joachim Giard Nolan Nichols Satrajit Ghosh Google Scholar Arno Klein Forrest Bao Eliezer Stavsky Yrjö Häme Joachim Giard Nolan Nichols Satrajit Ghosh PubMed Arno Klein Forrest Bao Eliezer Stavsky Yrjö Häme Joachim Giard Nolan Nichols Satrajit Ghosh Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.