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

PLOS ONE, Dec 2019

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.

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

May Fully Automated Whole-Head Segmentation with Improved Smoothness and Continuity, with Theory Reviewed Yu Huang 0 1 Lucas C. Parra 0 1 0 Department of Biomedical Engineering, City College of the City University of New York , New York, NY , USA 1 Academic Editor: Stefan Strack, University of Iowa, UNITED STATES 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 currentsource 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. - The increasing availability of magnetic resonance images (MR images, MRI) at 1 mm3 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 equivalentsMEG and TMS [18]. It is becoming increasingly clear that individual anatomy including complete cerebrospinal fluid (CSF), skull and scalp are key to obtain meaningful source localization in EEG and targeting of TES for individual subjects [1, 5, 912]. Unfortunately, currently available segmentation tools are of limited utility for this purpose. Statistical Parametric Mapping version 8 (SPM8, [13]) provides segmentation of the brain, CSF, skull and scalp using the Unified Segmentation algorithm [14], but its field of view (FOV) does not cover the whole head. FieldTrip [15] and the Brain Extraction Tool (BET, [16]) in the FMRIB Software Library (FSL, [17]) can extract the skull and scalp surfaces, but they also only operate on the standard FOV (the brain area only). The FMRIB Automated Segmentation Tool (FAST, [18]) and Integrated Registration and Segmentation Tool (FIRST, [19]) are designed, respectively, only for segmentation of brain tissues and subcortical structures. FreeSurfer [20 23], the ExpectationMaximization Segmentation tool (EMS, [24]), the Atlas Based Classification (ABC) and the EMSegmenter [2527] in 3D Slicer [28], BrainSuite [29, 30], BrainVISA Morphologist [31, 32], the CIVET segmentation [33], the Sub-Volume Probabilistic Atlases Segmentation tool (SVPASEG, [34]), and the segmentation module in BrainVoyager [35, 36] also only focus on brain tissues. 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, Grfelfing, Germany) and ScanIP (Simpleware Ltd, Exeter, UK), either are semi-automated or cannot generate (accurate) segmentation for CSF. Therefore, researchers have attempted several workarounds for the segmentation of the whole head for electromagnetic forward modeling, e.g., combination of different segmentation tools [2, 39], use of computed tomography (CT) images for skull segmentation [40], simple heuristics using thresholding and morphological operations [10, 38], addition of dummy model for the lower head and/or the neck [3], or manual segmentation [1]. In short, these approaches are either too specific, heuristic, or not fully automated for segmenting out CSF, skull, scalp and air cavities. In our view, the simplest way to adapt current automated segmentation tools for this purpose is to extend the atlas representing anatomical prior information to include those non-brain tis (...truncated)


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Yu Huang, Lucas C. Parra. Fully Automated Whole-Head Segmentation with Improved Smoothness and Continuity, with Theory Reviewed, PLOS ONE, 2015, Volume 10, Issue 5, DOI: 10.1371/journal.pone.0125477