Research
Print page Print page
Switch language
The Capital Region of Denmark - a part of Copenhagen University Hospital
Published

Skull segmentation from MR scans using a higher-order shape model based on convolutional restricted Boltzmann machines

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

DOI

  1. Classification of social anhedonia using temporal and spatial network features from a social cognition fMRI task

    Research output: Contribution to journalJournal articleResearchpeer-review

  2. European Ultrahigh-Field Imaging Network for Neurodegenerative Diseases (EUFIND)

    Research output: Contribution to journalJournal articleResearchpeer-review

  3. Neuroimaging biomarkers for clinical trials in atypical parkinsonian disorders: Proposal for a Neuroimaging Biomarker Utility System

    Research output: Contribution to journalJournal articleResearchpeer-review

  4. Accessibility of cortical regions to focal TES: Dependence on spatial position, safety, and practical constraints

    Research output: Contribution to journalJournal articleResearchpeer-review

View graph of relations

Transcranial brain stimulation (TBS) techniques such as transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS) and others have seen a strong increase as tools in therapy and research within the last 20 years. In order to precisely target the stimulation, it is important to accurately model the individual head anatomy of a subject. Of particular importance is accurate reconstruction of the skull, as it has the strongest impact on the current pathways due to its low conductivity. Thus providing automated tools, which can reliably reconstruct the anatomy of the human head from magnetic resonance (MR) scans would be highly valuable for the application of transcranial stimulation methods. These head models can also be used to inform source localization methods such as EEG and MEG. Automated segmentation of the skull from MR images is, however, challenging as the skull emits very little signal in MR. In order to avoid topological defects, such as holes in the segmentations, a strong model of the skull shape is needed. In this paper we propose a new shape model for skull segmentation based on the so-called convolutional restricted Boltzmann machines (cRBMs). Compared to traditionally used lower-order shape models, such as pair-wise Markov random fields (MRFs), the cRBMs model local shapes in larger spatial neighborhoods while still allowing for efficient inference. We compare the skull segmentation accuracy of our approach to two previously published methods and show significant improvement.

Original languageEnglish
Title of host publicationMedical Imaging 2018 : Image Processing
Volume10574
PublisherSPIE
Publication date2018
Article number1057404
ISBN (Electronic)9781510616370
DOIs
Publication statusPublished - 2018
EventMedical Imaging 2018: Image Processing - Houston, United States
Duration: 11 Feb 201813 Feb 2018

Conference

ConferenceMedical Imaging 2018: Image Processing
LandUnited States
ByHouston
Periode11/02/201813/02/2018
SponsorDECTRIS Ltd., The Society of Photo-Optical Instrumentation Engineers (SPIE)

Event

Medical Imaging 2018: Image Processing

11/02/201813/02/2018

Houston, United States

Event: Conference

    Research areas

  • head modeling, MRI, shape modeling, skull segmentation, transcranial brain stimulation

ID: 56438511