Lu, Ping; Barazzetti, Livia; Chandran, Vimal; Gerber, Kate; Weber, Stefan; Gerber, Nicolas; Reyes, Mauricio (2018). Highly accurate Facial Nerve Segmentation Refinement from CBCT/CT Imaging using a Super Resolution Classification Approach. IEEE transactions on biomedical engineering, 65(1), pp. 178-188. Institute of Electrical and Electronics Engineers IEEE 10.1109/TBME.2017.2697916
Full text not available from this repository. (Request a copy)Facial nerve segmentation is of considerable importance for pre-operative 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 sub-voxel classification level from CBCT/CT images. The super-resolution classification method learns the mapping from low-resolution CBCT/CT images to high-resolution facial nerve label images, obtained from manual segmentation on micro-CT images. We present preliminary results on dataset, 15 ex-vivo samples scanned including pairs of CBCT/CT scans and high-resolution micro-CT scans, with a Leave-One-Out (LOO) evaluation, and manual segmentations on micro-CT images as ground truth. Our experiments achieved a segmentation accuracy with a Dice coefficient of 0.818 ±0.052 , surface-to-surface distance of 0.121 ±0.030mm and Hausdorff distance of 0.715 ±0.169mm . We compared the proposed technique to two other semi-automated segmentation software tools, ITK-SNAP and GeoS, and show the ability of the proposed approach to yield sub-voxel levels of accuracy in delineating the facial nerve.
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