MOEMIL Laboratory, School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu 611731, China.
State Key Laboratory of Respiratory Disease, Department of Otolaryngology, Head & Neck Surgery, Laboratory of ENT-HNS Disease, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Comput Intell Neurosci. 2021 Sep 7;2021:9654059. doi: 10.1155/2021/9654059. eCollection 2021.
The vestibular system is the sensory apparatus that helps the body maintain its postural equilibrium, and semicircular canal is an important organ of the vestibular system. The semicircular canals are three membranous tubes, each forming approximately two-thirds of a circle with a diameter of approximately 6.5 mm, and segmenting them accurately is of great benefit for auxiliary diagnosis, surgery, and treatment of vestibular disease. However, the semicircular canal has small volume, which accounts for less than 1% of the overall computed tomography image. Doctors have to annotate the image in a slice-by-slice manner, which is time-consuming and labor-intensive. To solve this problem, we propose a novel 3D convolutional neural network based on 3D U-Net to automatically segment the semicircular canal. We added the spatial attention mechanism of 3D spatial squeeze and excitation modules, as well as channel attention mechanism of 3D global attention upsample modules to improve the network performance. Our network achieved an average dice coefficient of 92.5% on the test dataset, which shows competitive performance in semicircular canals segmentation task.
前庭系统是帮助人体维持姿势平衡的感觉器官，而半规管是前庭系统的重要器官。半规管是三个膜性管，每个管大约形成直径约为 6.5mm 的半圆形的三分之二，准确分割它们对半规管疾病的辅助诊断、手术和治疗非常有益。然而，半规管体积小，在整个 CT 图像中所占比例不到 1%。医生必须逐片注释图像，既耗时又费力。为了解决这个问题，我们提出了一种基于 3D U-Net 的新型 3D 卷积神经网络，用于自动分割半规管。我们在网络中添加了 3D 空间挤压和激励模块的空间注意力机制，以及 3D 全局注意力上采样模块的通道注意力机制，以提高网络性能。我们的网络在测试数据集上的平均骰子系数为 92.5%，在半规管分割任务中表现出了有竞争力的性能。
