A Hybrid Deep Registration of MR Scans to Interventional Ultrasound for Neurosurgical Guidance
Zeineldin RA, Karar ME, Mathis-Ullrich F, Burgert O (2021)
Publication Type: Conference contribution
Publication year: 2021
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12966 LNCS
Pages Range: 586-595
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Virtual, Online
ISBN: 9783030875886
DOI: 10.1007/978-3-030-87589-3_60
Abstract
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.
Authors with CRIS profile
Involved external institutions
Hochschule Reutlingen
Germany (DE)
Menoufia University (Minoufiya / Minufiya University) / جامعة المنوفية
Egypt (EG)
Karlsruhe Institute of Technology (KIT)
Germany (DE)
Germany (DE)
Menoufia University (Minoufiya / Minufiya University) / جامعة المنوفية
Egypt (EG)
Karlsruhe Institute of Technology (KIT)
Germany (DE)
How to cite
APA:
Zeineldin, R.A., Karar, M.E., Mathis-Ullrich, F., & Burgert, O. (2021). A Hybrid Deep Registration of MR Scans to Interventional Ultrasound for Neurosurgical Guidance. In Chunfeng Lian, Xiaohuan Cao, Islem Rekik, Xuanang Xu, Pingkun Yan (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 586-595). Virtual, Online: Springer Science and Business Media Deutschland GmbH.
MLA:
Zeineldin, Ramy A., et al. "A Hybrid Deep Registration of MR Scans to Interventional Ultrasound for Neurosurgical Guidance." Proceedings of the 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online Ed. Chunfeng Lian, Xiaohuan Cao, Islem Rekik, Xuanang Xu, Pingkun Yan, Springer Science and Business Media Deutschland GmbH, 2021. 586-595.
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