Towards accurate facial nerve segmentation with decoupling optimization

Abstract

Robotic cochlear implantation is an effective way to restore the hearing of hearing-impaired patients, and facial nerve recognition is the key to the operation. However, accurate facial nerve segmentation is a challenging task, mainly for two key issues: (1) the facial nerve area is very small in image, and there are many similar areas; (2) low contrast of the border between the facial nerve and the surrounding tissues increases the difficulty. In this work, we propose an end-to-end neural network, called FNSegNet, with two stages to solve these problems. Specifically, in the coarse segmentation stage, we first adopt three search identification modules to capture small objects by expanding the receptive field from high-level features and combine an effective pyramid fusion module to fuse. In the refine segmentation stage, we use a decoupling optimization module to establish the relationship between the central region and the boundary details of facial nerve by decoupling the boundary and center area. Meanwhile, we feed them into a spatial attention module to correct the conflict regions. Extensive experiments on the challenging dataset demonstrate that the proposed FNSegNet significantly improves the segmentation accuracy (0.858 on Dice, 0.363 mm on 95% Hausdorff distance), and reduces the computational complexity (13.33G on FLOPs, 9.86M parameters).