Abstract
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.
Highlights
The increasing availability of magnetic resonance images (MR images, magnetic resonance imaging (MRI)) at 1 mm3 resolution has made it possible to build realistic high-resolution models of individual human heads
Since the number of subjects is limited, the local tissue correlation map (TCM) and tissue probability map (TPM) cannot be generated from the true segmentation, we only evaluated the algorithm on the global TCM and used the TPM from our previous work
For each subject in the simulated data (Dataset I), we compared the performance of the proposed algorithm to the performance of the extended version of the Unified Segmentation (eUS) in Statistical Parametric Mapping 8 (SPM8), and the eUS with an Markov random fields (MRF)-based clean-up provided by SPM8
Summary
The increasing availability of magnetic resonance images (MR images, MRI) at 1 mm resolution has made it possible to build realistic high-resolution models of individual human heads. Accurate segmentations of the whole-head anatomy are important for the “forward modeling” of current flow in electroencephalography (EEG) and trancranial electric stimulation (TES), as well as their magnetic equivalents—MEG and TMS [1,2,3,4,5,6,7,8]. ITK-SNAP [37] and Neuroelectromagnetic Forward Head Modeling Toolbox (NFT, [38]) are semi-automated tools since they need user-specified seed point(s) to start Commercial software tools, such as ASA (ANT Software B.V., Enschede, Netherlands), Curry (Compumedics NeuroScan, Charlotte, NC), BESA (BESA GmbH, Gräfelfing, Germany) and ScanIP (Simpleware Ltd, Exeter, UK), either are semi-automated or cannot generate (accurate) segmentation for CSF
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