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Towards automated correction of brain shift using deep deformable magnetic resonance imaging-intraoperative ultrasound (MRI-iUS) registration

  • Intraoperative brain deformation, so called brain shift, affects the applicability of preoperative magnetic resonance imaging (MRI) data to assist the procedures of intraoperative ultrasound (iUS) guidance during neurosurgery. This paper proposes a deep learning-based approach for fast and accurate deformable registration of preoperative MRI to iUS images to correct brain shift. Based on the architecture of 3D convolutional neural networks, the proposed deep MRI-iUS registration method has been successfully tested and evaluated on the retrospective evaluation of cerebral tumors (RESECT) dataset. This study showed that our proposed method outperforms other registration methods in previous studies with an average mean squared error (MSE) of 85. Moreover, this method can register three 3D MRI-US pair in less than a second, improving the expected outcomes of brain surgery.

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Metadaten
Name:Zeineldin, Ramy; Burgert, Oliver
URN:urn:nbn:de:bsz:rt2-opus4-27906
DOI:https://doi.org/10.1515/cdbme-2020-0039
ISSN:2364-5504
Erschienen in:Current directions in biomedical engineering
Publisher:De Gruyter
Place of publication:Berlin
Document Type:Article
Language:English
Year of Publication:2020
Tag:MRI-iUS; biomedical image processing; brain shift; deep learning; image-guided neurosurgery
Volume:6
Issue:1
Pagenumber:5
First Page:1
Last Page:5
Dewey Decimal Classification:610 Medizin, Gesundheit
Open Access:Ja
Licence (German):License Logo  Creative Commons - CC BY - Namensnennung 4.0 International