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.

我们提出了一个不确定性感知的双流网络（UADSN），包括 2D 分割流和 3D 分割流，用于面神经在 CT 扫描中的定位和分割。通过使用两个流的预测结果来识别不确定区域，并引入一致性损失来监督这些区域的分割，同时在 U 型网络的跳跃连接中引入通道挤压和空间激发模块以提取有意义的空间信息。为了考虑拓扑保持，还引入了 clDice 损失函数。面神经数据集上的实验证明了 UADSN 及其子模块的有效性。