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

• Published in: NeuroImage (Orlando, Fla.) Vol. 174; pp. 587 - 598
• Main Authors: Nielsen, Jesper D., Madsen, Kristoffer H., Puonti, Oula, Siebner, Hartwig R., Bauer, Christian, Madsen, Camilla Gøbel, Saturnino, Guilherme B., Thielscher, Axel
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.07.2018; Elsevier Limited
• Subjects: Accuracy; Bandwidths; Bone imaging; Bone surgery; Computed tomography; EEG; Electric fields; Electrical stimulation of the brain; Electroencephalography; ESB; Forward modeling; Image processing; Magnetic resonance imaging; Magnetoencephalography; Medical imaging; Neck; Neuroimaging; NMR; Nuclear magnetic resonance; Open source software; Quality; Segmentation; Skull; Skull segmentation; Software packages; Spine; Standard deviation; Transcranial brain stimulation; Vertebrae; Volume conductor model; Skull segmentation; Electroencephalography; Volume conductor model; Forward modeling; Transcranial brain stimulation
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2018.03.001

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. •Assessment of three methods for the automatic skull segmentation from MR images.•Rigorous test of their accuracy by comparison against CT data of the same subjects.•FSL and SPM12 can achieve reasonable accuracy for the upper part of the head.•A combination of T1- and T2-weighted images, rather than a single T1, is suggested.•Accuracy strongly benefits from optimization of the MRI sequence parameters.