TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Zeineldin, Ramy
A1  - Karar, Mohamed
A1  - Mathis-Ullrich, Franziska
A1  - Burgert, Oliver
ED  - Lian, Chunfeng
ED  - Cao, Xiaohuan
ED  - Rekik, Islem
ED  - Xu, Xuanang
ED  - Yan, Pingkun
T1  - A hybrid deep registration of MR scans to interventional ultrasound for neurosurgical guidance
T2  - Machine Learning in Medical Imaging : 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings (Lecture Notes in Computer Science, vol 12966)
N2  - Despite the recent advances in image-guided neurosurgery, reliable and accurate estimation of the brain shift still remains one of the key challenges. In this paper, we propose an automated multimodal deformable registration method using hybrid learning-based and classical approaches to improve neurosurgical procedures. Initially, the moving and fixed images are aligned using classical affine transformation (MINC toolkit), and then the result is provided to the convolutional neural network, which predicts the deformation field using backpropagation. Subsequently, the moving image is transformed using the resultant deformation into a moved image. Our model was evaluated on two publicly available datasets: the retrospective evaluation of cerebral tumors (RESECT) and brain images of tumors for evaluation (BITE). The mean target registration errors have been reduced from 5.35 ± 4.29 to 0.99 ± 0.22 mm in the RESECT and from 4.18 ± 1.91 to 1.68 ± 0.65 mm in the BITE. Experimental results showed that our method improved the state-of-the-art in terms of both accuracy and runtime speed (170 ms on average). Hence, the proposed method provides a fast runtime for 3D MRI to intra-operative US pair in a GPU-based implementation, which shows a promise for its applicability in assisting the neurosurgical procedures compensating for brain shift.
KW  - brain shift
KW  - computer-aided diagnosis
KW  - deformable
KW  - MRI-US registration
KW  - deep learning
Y1  - 2021
SN  - 978-3-030-87589-3
SB  - 978-3-030-87589-3
U6  - https://doi.org/10.1007/978-3-030-87589-3_60
DO  - https://doi.org/10.1007/978-3-030-87589-3_60
SP  - 586
EP  - 595
S1  - 10
PB  - Springer
CY  - Cham
ER  -