Highly Accurate Facial Nerve Segmentation Refinement From CBCT/CT Imaging Using a Super-Resolution Classification Approach

@article{Lu2018HighlyAF,
  title={Highly Accurate Facial Nerve Segmentation Refinement From CBCT/CT Imaging Using a Super-Resolution Classification Approach},
  author={Ping Lu and Livia Barazzetti and Vimal Chandran and Kate A. Gavaghan and Stefan Weber and Nicolas Gerber and Mauricio Reyes},
  journal={IEEE Transactions on Biomedical Engineering},
  year={2018},
  volume={65},
  pages={178-188}
}
Facial nerve segmentation is of considerable importance for preoperative planning of cochlear implantation. However, it is strongly influenced by the relatively low resolution of the cone-beam computed tomography (CBCT) images used in clinical practice. In this paper, we propose a super-resolution classification method, which refines a given initial segmentation of the facial nerve to a subvoxel classification level from CBCT/CT images. The super-resolution classification method learns the… CONTINUE READING