Automatic skull segmentation from MR images for realistic volume conductor models of the head: Assessment of the state-of-the-art

Jesper Duemose Nielsen, Kristoffer Hougaard Madsen, Oula Puonti, Hartwig R. Siebner, Christian Bauer, Camilla Gøbel Madsen, Guilherme B. Saturnino, Axel Thielscher*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Anatomically realistic volume conductor models of the human head are important for accurate forward modeling of the electric field during transcranial brain stimulation (TBS), electro- (EEG) and magnetoencephalography (MEG). In particular, the skull compartment exerts a strong influence on the field distribution due to its low conductivity, suggesting the need to represent its geometry accurately. However, automatic skull reconstruction from structural magnetic resonance (MR) images is difficult, as compact bone has a very low signal in magnetic resonance imaging (MRI). Here, we evaluate three methods for skull segmentation, namely FSL BET2, the unified segmentation routine of SPM12 with extended spatial tissue priors, and the skullfinder tool of BrainSuite. To our knowledge, this study is the first to rigorously assess the accuracy of these state-of-the-art tools by comparison with CT-based skull segmentations on a group of ten subjects. We demonstrate several key factors that improve the segmentation quality, including the use of multi-contrast MRI data, the optimization of the MR sequences and the adaptation of the parameters of the segmentation methods. We conclude that FSL and SPM12 achieve better skull segmentations than BrainSuite. The former methods obtain reasonable results for the upper part of the skull when a combination of T1- and T2-weighted images is used as input. The SPM12-based results can be improved slightly further by means of simple morphological operations to fix local defects. In contrast to FSL BET2, the SPM12-based segmentation with extended spatial tissue priors and the BrainSuite-based segmentation provide coarse reconstructions of the vertebrae, enabling the construction of volume conductor models that include the neck. We exemplarily demonstrate that the extended models enable a more accurate estimation of the electric field distribution during transcranial direct current stimulation (tDCS) for montages that involve extraencephalic electrodes. The methods provided by FSL and SPM12 are integrated into pipelines for the automatic generation of realistic head models based on tetrahedral meshes, which are distributed as part of the open-source software package SimNIBS for field calculations for transcranial brain stimulation.
Original languageEnglish
JournalNeuroImage
Volume174
Pages (from-to)587-598
ISSN1053-8119
DOIs
Publication statusPublished - 2018

Keywords

  • Neurology
  • Cognitive Neuroscience
  • Electroencephalography
  • Forward modeling
  • Skull segmentation
  • Transcranial brain stimulation
  • Volume conductor model

Cite this

Nielsen, Jesper Duemose ; Madsen, Kristoffer Hougaard ; Puonti, Oula ; Siebner, Hartwig R. ; Bauer, Christian ; Madsen, Camilla Gøbel ; Saturnino, Guilherme B. ; Thielscher, Axel. / Automatic skull segmentation from MR images for realistic volume conductor models of the head: Assessment of the state-of-the-art. In: NeuroImage. 2018 ; Vol. 174. pp. 587-598.
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title = "Automatic skull segmentation from MR images for realistic volume conductor models of the head: Assessment of the state-of-the-art",
abstract = "Anatomically realistic volume conductor models of the human head are important for accurate forward modeling of the electric field during transcranial brain stimulation (TBS), electro- (EEG) and magnetoencephalography (MEG). In particular, the skull compartment exerts a strong influence on the field distribution due to its low conductivity, suggesting the need to represent its geometry accurately. However, automatic skull reconstruction from structural magnetic resonance (MR) images is difficult, as compact bone has a very low signal in magnetic resonance imaging (MRI). Here, we evaluate three methods for skull segmentation, namely FSL BET2, the unified segmentation routine of SPM12 with extended spatial tissue priors, and the skullfinder tool of BrainSuite. To our knowledge, this study is the first to rigorously assess the accuracy of these state-of-the-art tools by comparison with CT-based skull segmentations on a group of ten subjects. We demonstrate several key factors that improve the segmentation quality, including the use of multi-contrast MRI data, the optimization of the MR sequences and the adaptation of the parameters of the segmentation methods. We conclude that FSL and SPM12 achieve better skull segmentations than BrainSuite. The former methods obtain reasonable results for the upper part of the skull when a combination of T1- and T2-weighted images is used as input. The SPM12-based results can be improved slightly further by means of simple morphological operations to fix local defects. In contrast to FSL BET2, the SPM12-based segmentation with extended spatial tissue priors and the BrainSuite-based segmentation provide coarse reconstructions of the vertebrae, enabling the construction of volume conductor models that include the neck. We exemplarily demonstrate that the extended models enable a more accurate estimation of the electric field distribution during transcranial direct current stimulation (tDCS) for montages that involve extraencephalic electrodes. The methods provided by FSL and SPM12 are integrated into pipelines for the automatic generation of realistic head models based on tetrahedral meshes, which are distributed as part of the open-source software package SimNIBS for field calculations for transcranial brain stimulation.",
keywords = "Neurology, Cognitive Neuroscience, Electroencephalography, Forward modeling, Skull segmentation, Transcranial brain stimulation, Volume conductor model",
author = "Nielsen, {Jesper Duemose} and Madsen, {Kristoffer Hougaard} and Oula Puonti and Siebner, {Hartwig R.} and Christian Bauer and Madsen, {Camilla G{\o}bel} and Saturnino, {Guilherme B.} and Axel Thielscher",
year = "2018",
doi = "10.1016/j.neuroimage.2018.03.001",
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pages = "587--598",
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issn = "1053-8119",
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Automatic skull segmentation from MR images for realistic volume conductor models of the head: Assessment of the state-of-the-art. / Nielsen, Jesper Duemose; Madsen, Kristoffer Hougaard; Puonti, Oula; Siebner, Hartwig R.; Bauer, Christian; Madsen, Camilla Gøbel; Saturnino, Guilherme B.; Thielscher, Axel.

In: NeuroImage, Vol. 174, 2018, p. 587-598.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Automatic skull segmentation from MR images for realistic volume conductor models of the head: Assessment of the state-of-the-art

AU - Nielsen, Jesper Duemose

AU - Madsen, Kristoffer Hougaard

AU - Puonti, Oula

AU - Siebner, Hartwig R.

AU - Bauer, Christian

AU - Madsen, Camilla Gøbel

AU - Saturnino, Guilherme B.

AU - Thielscher, Axel

PY - 2018

Y1 - 2018

N2 - Anatomically realistic volume conductor models of the human head are important for accurate forward modeling of the electric field during transcranial brain stimulation (TBS), electro- (EEG) and magnetoencephalography (MEG). In particular, the skull compartment exerts a strong influence on the field distribution due to its low conductivity, suggesting the need to represent its geometry accurately. However, automatic skull reconstruction from structural magnetic resonance (MR) images is difficult, as compact bone has a very low signal in magnetic resonance imaging (MRI). Here, we evaluate three methods for skull segmentation, namely FSL BET2, the unified segmentation routine of SPM12 with extended spatial tissue priors, and the skullfinder tool of BrainSuite. To our knowledge, this study is the first to rigorously assess the accuracy of these state-of-the-art tools by comparison with CT-based skull segmentations on a group of ten subjects. We demonstrate several key factors that improve the segmentation quality, including the use of multi-contrast MRI data, the optimization of the MR sequences and the adaptation of the parameters of the segmentation methods. We conclude that FSL and SPM12 achieve better skull segmentations than BrainSuite. The former methods obtain reasonable results for the upper part of the skull when a combination of T1- and T2-weighted images is used as input. The SPM12-based results can be improved slightly further by means of simple morphological operations to fix local defects. In contrast to FSL BET2, the SPM12-based segmentation with extended spatial tissue priors and the BrainSuite-based segmentation provide coarse reconstructions of the vertebrae, enabling the construction of volume conductor models that include the neck. We exemplarily demonstrate that the extended models enable a more accurate estimation of the electric field distribution during transcranial direct current stimulation (tDCS) for montages that involve extraencephalic electrodes. The methods provided by FSL and SPM12 are integrated into pipelines for the automatic generation of realistic head models based on tetrahedral meshes, which are distributed as part of the open-source software package SimNIBS for field calculations for transcranial brain stimulation.

AB - Anatomically realistic volume conductor models of the human head are important for accurate forward modeling of the electric field during transcranial brain stimulation (TBS), electro- (EEG) and magnetoencephalography (MEG). In particular, the skull compartment exerts a strong influence on the field distribution due to its low conductivity, suggesting the need to represent its geometry accurately. However, automatic skull reconstruction from structural magnetic resonance (MR) images is difficult, as compact bone has a very low signal in magnetic resonance imaging (MRI). Here, we evaluate three methods for skull segmentation, namely FSL BET2, the unified segmentation routine of SPM12 with extended spatial tissue priors, and the skullfinder tool of BrainSuite. To our knowledge, this study is the first to rigorously assess the accuracy of these state-of-the-art tools by comparison with CT-based skull segmentations on a group of ten subjects. We demonstrate several key factors that improve the segmentation quality, including the use of multi-contrast MRI data, the optimization of the MR sequences and the adaptation of the parameters of the segmentation methods. We conclude that FSL and SPM12 achieve better skull segmentations than BrainSuite. The former methods obtain reasonable results for the upper part of the skull when a combination of T1- and T2-weighted images is used as input. The SPM12-based results can be improved slightly further by means of simple morphological operations to fix local defects. In contrast to FSL BET2, the SPM12-based segmentation with extended spatial tissue priors and the BrainSuite-based segmentation provide coarse reconstructions of the vertebrae, enabling the construction of volume conductor models that include the neck. We exemplarily demonstrate that the extended models enable a more accurate estimation of the electric field distribution during transcranial direct current stimulation (tDCS) for montages that involve extraencephalic electrodes. The methods provided by FSL and SPM12 are integrated into pipelines for the automatic generation of realistic head models based on tetrahedral meshes, which are distributed as part of the open-source software package SimNIBS for field calculations for transcranial brain stimulation.

KW - Neurology

KW - Cognitive Neuroscience

KW - Electroencephalography

KW - Forward modeling

KW - Skull segmentation

KW - Transcranial brain stimulation

KW - Volume conductor model

U2 - 10.1016/j.neuroimage.2018.03.001

DO - 10.1016/j.neuroimage.2018.03.001

M3 - Journal article

VL - 174

SP - 587

EP - 598

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

ER -