IRegNet: Non-rigid registration of MRI to interventional US for brain-shift compensation using convolutional neural networks

Zeineldin RA, Karar ME, Elshaer Z, Schmidhammer M, Coburger J, Wirtz CR, Burgert O, Mathis-Ullrich F (2021)

Publication Type: Journal article

Publication year: 2021

Journal

IEEE Access Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 9

Pages Range: 147579-147590

DOI: 10.1109/ACCESS.2021.3120306

Abstract

Accurate and safe neurosurgical intervention can be affected by intra-operative tissue deformation, known as brain-shift. In this study, we propose an automatic, fast, and accurate deformable method, called iRegNet, for registering pre-operative magnetic resonance images to intra-operative ultrasound volumes to compensate for brain-shift. iRegNet is a robust end-to-end deep learning approach for the non-linear registration of MRI-iUS images in the context of image-guided neurosurgery. Pre-operative MRI (as moving image) and iUS (as fixed image) are first appended to our convolutional neural network, after which a non-rigid transformation field is estimated. The MRI image is then transformed using the output displacement field to the iUS coordinate system. Extensive experiments have been conducted on two multi-location databases, which are the BITE and the RESECT. Quantitatively, iRegNet reduced the mean landmark errors from pre-registration value of (4.18 ± 1.84 and 5.35 ± 4.19 mm) to the lowest value of (1.47 ± 0.61 and 0.84 ± 0.16 mm) for the BITE and RESECT datasets, respectively. Additional qualitative validation of this study was conducted by two expert neurosurgeons through overlaying MRI-iUS pairs before and after the deformable registration. Experimental findings show that our proposed iRegNet is fast and achieves state-of-the-art accuracies outperforming state-of-the-art approaches. Furthermore, the proposed iRegNet can deliver competitive results, even in the case of non-trained images as proof of its generality and can therefore be valuable in intra-operative neurosurgical guidance.

Authors with CRIS profile

Franziska Mathis-Ullrich

Involved external institutions

Karlsruhe Institute of Technology (KIT) DE Germany (DE) Menoufia University (Minoufiya / Minufiya University) / جامعة المنوفية‎ EG Egypt (EG) Universität Ulm DE Germany (DE) Hochschule Reutlingen DE Germany (DE)

How to cite

APA:

Zeineldin, R.A., Karar, M.E., Elshaer, Z., Schmidhammer, M., Coburger, J., Wirtz, C.R.,... Mathis-Ullrich, F. (2021). IRegNet: Non-rigid registration of MRI to interventional US for brain-shift compensation using convolutional neural networks. IEEE Access, 9, 147579-147590. https://doi.org/10.1109/ACCESS.2021.3120306

MLA:

Zeineldin, Ramy A., et al. "IRegNet: Non-rigid registration of MRI to interventional US for brain-shift compensation using convolutional neural networks." IEEE Access 9 (2021): 147579-147590.

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