  Facial nerve segmentation is crucial for preoperative path planning in
cochlear implantation surgery. Recently, researchers have proposed some
segmentation methods, such as atlas-based and deep learning-based methods.
However, since the facial nerve is a tubular organ with a diameter of only
1.0-1.5mm, it is challenging to locate and segment the facial nerve in CT
scans. In this work, we propose an uncertainty-aware dualstream network
(UADSN). UADSN consists of a 2D segmentation stream and a 3D segmentation
stream. Predictions from two streams are used to identify uncertain regions,
and a consistency loss is employed to supervise the segmentation of these
regions. In addition, we introduce channel squeeze & spatial excitation modules
into the skip connections of U-shaped networks to extract meaningful spatial
information. In order to consider topologypreservation, a clDice loss is
introduced into the supervised loss function. Experimental results on the
facial nerve dataset demonstrate the effectiveness of UADSN and our submodules.
