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SeUneter: Channel attentive U-Net for instance segmentation of the cervical spine MRI medical image

Authors :
Xiang Zhang
Yi Yang
Yi-Wei Shen
Ping Li
Yuan Zhong
Jing Zhou
Ke-Rui Zhang
Chang-Yong Shen
Yi Li
Meng-Fei Zhang
Long-Hai Pan
Li-Tai Ma
Hao Liu
Source :
Frontiers in Physiology, Vol 13 (2022)
Publication Year :
2022
Publisher :
Frontiers Media S.A., 2022.

Abstract

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 makes 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 the MRI T2-weighted (T2W) 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 structure based on the original U-Net and added a channel attention module to the double convolution of the feature extraction. SeUneter could enhance the semantic information of the segmentation and weaken the characteristic information of non-segmentation to the 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. Prior knowledge of the data was used to optimize the segmentation results by a post-process to improve the segmentation performance. The mean of Intersection Over Union (mIOU) was calculated for the different categories, while the mean of the 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’s performance was superior to U-Net, AttU-Net, UNet++, DeepLab-v3+, TransUNet, and Swin-Unet on the spinal cord with mIOU of 86.34% and the spinal canal with mIOU of 73.44%. The SeUneter matched or exceeded the performance of the aforementioned segmentation models when segmenting vertebral bodies or intervertebral discs. Among all models, SeUneter achieved the highest mIOU and mDSC of 82.73% and 90.66%, respectively, for the whole cervical spine.

Details

Language :
English
ISSN :
1664042X
Volume :
13
Database :
Directory of Open Access Journals
Journal :
Frontiers in Physiology
Publication Type :
Academic Journal
Accession number :
edsdoj.56310af04da1420b9570f7d56a32186f
Document Type :
article
Full Text :
https://doi.org/10.3389/fphys.2022.1081441