Conference paper
Skull segmentation from MR scans using a higher-order shape model based on convolutional restricted Boltzmann machines
Department of Electrical Engineering, Technical University of Denmark1
Center for Magnetic Resonance, Department of Electrical Engineering, Technical University of Denmark2
Department of Applied Mathematics and Computer Science, Technical University of Denmark3
Visual Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark4
Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark5
Hvidovre Hospital6
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.
Language: | English |
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Publisher: | SPIE - International Society for Optical Engineering |
Year: | 2018 |
Pages: | 1057404-1057404-9 |
Proceedings: | SPIE Medical Imaging 2018 |
Series: | Proceedings of Spie - the International Society for Optical Engineering |
ISBN: | 1510616373 , 1510616381 , 9781510616370 and 9781510616387 |
ISSN: | 24109045 , 16057422 , 1996756x and 0277786x |
Types: | Conference paper |
DOI: | 10.1117/12.2293073 |
ORCIDs: | Puonti, Oula , Van Leemput, Koen , Nielsen, Jesper Duemose , Madsen, Kristoffer Hougaard and Thielscher, Axel |