Name
Title | ||
|---|---|---|
iRegNet: Non-Rigid Registration of MRI to Interventional US for Brain-Shift Compensation Using Convolutional Neural Networks |
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ACCESS.2021.3120306 | IEEE ACCESS |
Keywords | DocType | Volume |
Magnetic resonance imaging, Neurosurgery, Strain, Three-dimensional displays, Imaging, Image registration, Deep learning, Brain-Shift, computer-aided diagnosis, medical image registration, neurosurgery, intra-operative ultrasound | Journal | 9 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ramy A. Zeineldin | 1 | 7 | 1.53 |
| Mohamed E. Karar | 2 | 0 | 0.34 |
| Ziad Elshaer | 3 | 0 | 0.34 |
| Markus Schmidhammer | 4 | 0 | 0.34 |
| Jan Coburger | 5 | 0 | 0.34 |
| Christian R. Wirtz | 6 | 0 | 0.34 |
| Oliver Burgert | 7 | 0 | 0.34 |
| Franziska Mathis-Ullrich | 8 | 1 | 1.39 |