TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Zeineldin, Ramy
A1  - Karar, Mohamed Esmail
A1  - Coburger, Jan
A1  - Wirtz, Christian
A1  - Mathis-Ullrich, Franziska
A1  - Burgert, Oliver
T1  - Towards automated correction of brain shift using deep deformable magnetic resonance imaging-intraoperative ultrasound (MRI-iUS) registration
JF  - Current directions in biomedical engineering
N2  - 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.
KW  - biomedical image processing
KW  - brain shift
KW  - deep learning
KW  - image-guided neurosurgery
KW  - MRI-iUS
Y1  - 2020
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-27906
SN  - 2364-5504
SS  - 2364-5504
U6  - https://doi.org/10.1515/cdbme-2020-0039
DO  - https://doi.org/10.1515/cdbme-2020-0039
VL  - 6
IS  - 1
SP  - 1
EP  - 5
S1  - 5
PB  - De Gruyter
CY  - Berlin
ER  -