SeUneter: Channel attentive U-Net for instance segmentation of the cervical spine MRI medical image - Zhang, Xiang; Yang, Yi; Shen, Yi-Wei; Li, Ping; Zhong, Yuan; Zhou, Jing; Zhang, Ke-Rui; Shen, Chang-Yong; Li, Yi; Zhang, Meng-Fei; Pan, Long-Hai; Ma, Li-Tai; et al. (Frontiers in Physiology, 2022)
In recent years, cervical spondylosis has become one of the most common chronic diseases and has received much attention from the public. Magnetic resonance imaging (MRI) is the most widely used imaging modality for the diagnosis of degenerative cervical spondylosis. The manual identification and segmentation of the cervical spine on MRI make it a laborious, time-consuming, and error-prone process. In this work, we collected a new dataset of 300 patients with a total of 600 cervical spine images in MRI T2 modality for the first time, which included the cervical spine, intervertebral discs, spinal cord, and spinal canal information. A new instance segmentation approach called SeUneter was proposed for cervical spine segmentation. SeUneter expanded the depth of the network struct based on the original U-Net and added a channel attention module to the double convolution of the feature extraction. SeUneter could enhance semantic information for the segmentation and weaken the characteristic information of non-segmentation to screen for important feature channels in double convolution. In the meantime, to alleviate the over-fitting of the model under insufficient samples, the Cutout was used to crop the pixel information in the original image at random positions of a fixed size, and the number of training samples in the original data was increased. The prior knowledge of the data was used to optimize the segmentation results by post-process to improve the segmentation performance. The mean of intersection Over Union (mIOU) was calculated for the different categories, while the mean of dice similarity coefficient (mDSC), and mIOU were calculated to compare the segmentation results of different deep learning models for all categories. Compared with multiple models under the same experimental settings, our proposed SeUneter performed superior to U-Net, AttUnet, Unet++, Deeplab v3+, Transunet, Swin-Unet on the spinal cord with mIOU of 86.34%, and spinal canal with mIOU of 73.44%. The SeUneter matched or exceeded the performance of the above segmentation models when segmenting vertebral bodies or intervertebral discs. Among all models, the SeUneter achieved the highest mIOU and mDSC of 82.73% and 90.66%, respectively, for the whole cervical spine.