Fully Automated Whole-Head Segmentation with Improved Smoothness and Continuity, with Theory Reviewed

• Published in: PloS one Vol. 10; no. 5; p. e0125477
• Main Authors: Huang, Yu, Parra, Lucas C.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 18.05.2015; Public Library of Science (PLoS)
• Subjects: Algorithms; Anatomy; Automation; Bayes Theorem; Bayesian analysis; Biomedical engineering; Brain; Brain - anatomy & histology; Brain Mapping; Cerebrospinal fluid; Computer simulation; Constraint modelling; Continuity (mathematics); EEG; Electroencephalography; Field of view; Head; Head - anatomy & histology; Humans; Image acquisition; Image processing; Image segmentation; Imaging, Three-Dimensional - methods; Imaging, Three-Dimensional - statistics & numerical data; Magnetic fields; Magnetic resonance; Magnetic Resonance Imaging; Magnetoencephalography; Magnetorheological fluids; Markov Chains; Mathematical models; Medical imaging; Medical research; Methods; Models, Anatomic; Models, Neurological; Models, Statistical; Morphology; Neuroimaging; Registration; Reviews; Smoothness; Soft tissues; Software; Software development tools; Statistical inference; Statistical mechanics; Tissues; Tomography; Transcranial magnetic stimulation
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0125477

Individualized current-flow models are needed for precise targeting of brain structures using transcranial electrical or magnetic stimulation (TES/TMS). The same is true for current-source reconstruction in electroencephalography and magnetoencephalography (EEG/MEG). The first step in generating such models is to obtain an accurate segmentation of individual head anatomy, including not only brain but also cerebrospinal fluid (CSF), skull and soft tissues, with a field of view (FOV) that covers the whole head. Currently available automated segmentation tools only provide results for brain tissues, have a limited FOV, and do not guarantee continuity and smoothness of tissues, which is crucially important for accurate current-flow estimates. Here we present a tool that addresses these needs. It is based on a rigorous Bayesian inference framework that combines image intensity model, anatomical prior (atlas) and morphological constraints using Markov random fields (MRF). The method is evaluated on 20 simulated and 8 real head volumes acquired with magnetic resonance imaging (MRI) at 1 mm3 resolution. We find improved surface smoothness and continuity as compared to the segmentation algorithms currently implemented in Statistical Parametric Mapping (SPM). With this tool, accurate and morphologically correct modeling of the whole-head anatomy for individual subjects may now be feasible on a routine basis. Code and data are fully integrated into SPM software tool and are made publicly available. In addition, a review on the MRI segmentation using atlas and the MRF over the last 20 years is also provided, with the general mathematical framework clearly derived.