Automatic skull segmentation from MR images for realistic volume conductor models of the head: Assessment of the state-of-the-art
Introduction
Volume conductor models of the head are key components of several neuroscientific methods such as electric field simulations for transcranial brain stimulation (TBS) and source localization in electro- (EEG) and magnetoencephalography (MEG). The anatomical accuracy of the head models has a strong influence on the accuracy of the calculated field distributions (Cho et al., 2015; Dannhauer et al., 2011; Eichelbaum et al., 2014; Lanfer et al., 2012; Montes-Restrepo et al., 2014; Wolters et al., 2006) and attempts to use individualized models based on structural magnetic resonance (MR) images are gaining momentum (Vorwerk et al., 2014). Recently available open-source software, including FSL (Smith et al., 2004), BrainSuite (Shattuck and Leahy, 2002), and SPM12 (http://www.fil.ion.ucl.ac.uk/spm/), facilitates the adoption of this approach by offering automatic segmentation procedures for the head. These tools have been integrated into software pipelines for the forward modeling of electric fields for TBS (e.g., SimNIBS; Thielscher et al., 2015) and EEG/MEG (e.g., FieldTrip; Oostenveld et al., 2011 and Brainstorm; Tadel et al., 2011). Accurate modeling of the skull compartment is an important aspect of individualized head models as the skull strongly shapes the forward solution due to its low conductivity (Dannhauer et al., 2011; Indahlastari et al., 2016; Lanfer et al., 2012; Montes-Restrepo et al., 2014; Stenroos et al., 2014). However, its automatic segmentation is still a major challenge, as the compact bone parts have a very low signal in conventional magnetic resonance imaging (MRI) sequences.
While the performance of most software packages in segmenting the brain have been thoroughly validated, similar tests are scarce for the skull. Thus, in this study we investigate the performance of three widely used neuroimaging software packages, FSL (Smith et al., 2004), BrainSuite (Shattuck and Leahy, 2002), and SPM12 (http://www.fil.ion.ucl.ac.uk/spm/). Specifically, we assess FSL BET2 which includes the BET and betsurf tools (Pechaud et al., 2006), BrainSuite skullfinder (Dogdas et al., 2005), and the unified segmentation routine (Ashburner and Friston, 2005) implemented in SPM12. The latter was tested with spatially extended tissue priors in order to avoid clipping of the lower parts of the head (Huang et al., 2013). In contrast to BrainSuite, FSL and SPM12 support the use of multi-spectral MRI for segmentation. We therefore also compare the results when basing the segmentations on a single, high-resolution T1-weighted structural MR image, as often acquired in neuroimaging studies and used in clinical standard of care, versus a combination of high-resolution T1- and T2-weighted MR images. In addition, for the SPM12-based segmentations, we assess to which extent the results can be improved when applying morphological operations to “clean up” the raw segmentations. We test the quality of the segmentations by systematic comparisons against skull segmentations from computed tomography (CT) scans of the same subjects. To the best of our knowledge, this study is the first to rigorously assess the performance of these tools on skull segmentation and thus serves as important evaluation of the state-of-the-art on this topic.
Whereas the main focus of the paper is on skull segmentation, we further compare the accuracy of the reconstructed brain surfaces derived from SPM12-based segmentations with surfaces obtained using FreeSurfer 5.3.0 (Dale et al., 1999; Fischl et al., 1999). Finally, we exemplarily demonstrate the importance of selecting adequate MRI sequence parameters and adjusting the parameters of the SPM12 segmentation routine to the properties of the MR images in order to achieve robust and accurate results, particularly in non-brain regions. As such, our study gives useful guidelines for the adoption of individualized volume conductor models in neuroscientific research.
Section snippets
Subjects
Ten healthy subjects (five Caucasians [three males], five East Asians [two males], 20–50 years old; 28.9 ± 9.3 [mean age ± SD]) were included in this study. They had no previous history of neurological or psychiatric disorders and were screened for contraindications to MRI and CT. In addition, the structural MR images were checked by a radiologist. Written informed consent was obtained from all participants prior to the scans. The study was approved by the Ethical Committee of the Capital
MRI-based skull reconstructions
Comparison of the MRI- with the CT-based segmentations reveals better results for the segmentations based on combined T1w- and T2w-images versus those using only a T1w image (Fig. 1), consistently for FSL BET2 and the unified segmentation routine of SPM12. Inclusion of a T2w image generally serves to improve the segmentations and stabilizes the results (i.e., decrease the variance across subjects). Importantly, outliers with very bad segmentations are mostly prevented. This is likely due to the
Discussion
We have validated the accuracy of skull segmentations obtained by three methods (FSL BET2, BrainSuite skullfinder, and the SPM12 unified segmentation routine) by comparing against CT-based skull segmentations in ten subjects. Both FSL and SPM12 give reasonable results for the upper part of the skull, in particular when both a T1w and T2w image are used as input. The results of BrainSuite are less accurate. For FSL and SPM12, including a T2w image strongly reduces the variability of the
Conclusion
In summary, our study demonstrates the current state-of-the-art of automatic skull segmentation from MR images, including the identification of remaining shortcomings, and introduces a novel, easily accessible and validated open-source tool for the automatic creation of volume meshes of the complete head. We have compared three methods (FSL BET2, BrainSuite skullfinder, and the unified segmentation routine of SPM12 with extended spatial tissue priors) to automatically segment the human skull.
Acknowledgements
This study was supported by the Lundbeck foundation (grant R118-A11308 to AT and grant R59-A5399 - PI Hartwig Siebner), the Novonordisk foundation (grant no. NNF14OC0011413) and a PhD stipend of the Sino-Danish Center to JDN.
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