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Automatic Semicircular Canal Segmentation of CT Volumes Using Improved 3D U-Net with Attention Mechanism
Computational Intelligence and Neuroscience ( IF 3.120 ) Pub Date : 2021-09-08 , DOI: 10.1155/2021/9654059
Hongcheng Wu 1 , Juanxiu Liu 1 , Gui Chen 2 , Weixing Liu 2 , Ruqian Hao 1 , Lin Liu 1 , Guangming Ni 1 , Yong Liu 1 , Xiaowen Zhang 2 , Jing Zhang 1
Affiliation  

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.

中文翻译:

使用改进的 3D U-Net 和注意力机制自动对 CT 体积进行半规管分割

前庭系统是帮助身体保持姿势平衡的感觉器官,半规管是前庭系统的重要器官。半规管是三个膜管,每个管形成约三分之二的圆,直径约6.5毫米,准确分割它们对前庭疾病的辅助诊断、手术和治疗有很大的好处。然而,半规管体积小,占整个计算机断层扫描图像的不到1%。医生必须以逐片的方式对图像进行注释,这既费时又费力。为了解决这个问题,我们提出了一种基于 3D U-Net 的新型 3D 卷积神经网络来自动分割半规管。我们添加了 3D 空间挤压和激发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实现了 92.5% 的平均骰子系数,这显示了在半规管分割任务中的竞争性能。
更新日期:2021-09-08
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