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

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 se...

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Autores principales: 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, Liu, Hao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9763568/
https://www.ncbi.nlm.nih.gov/pubmed/36561215
http://dx.doi.org/10.3389/fphys.2022.1081441
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author 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
Liu, Hao
author_facet 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
Liu, Hao
author_sort Zhang, Xiang
collection PubMed
description 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.
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spelling pubmed-97635682022-12-21 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 Liu, Hao Front Physiol Physiology 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. Frontiers Media S.A. 2022-12-06 /pmc/articles/PMC9763568/ /pubmed/36561215 http://dx.doi.org/10.3389/fphys.2022.1081441 Text en Copyright © 2022 Zhang, Yang, Shen, Li, Zhong, Zhou, Zhang, Shen, Li, Zhang, Pan, Ma and Liu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
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
Liu, Hao
SeUneter: Channel attentive U-Net for instance segmentation of the cervical spine MRI medical image
title SeUneter: Channel attentive U-Net for instance segmentation of the cervical spine MRI medical image
title_full SeUneter: Channel attentive U-Net for instance segmentation of the cervical spine MRI medical image
title_fullStr SeUneter: Channel attentive U-Net for instance segmentation of the cervical spine MRI medical image
title_full_unstemmed SeUneter: Channel attentive U-Net for instance segmentation of the cervical spine MRI medical image
title_short SeUneter: Channel attentive U-Net for instance segmentation of the cervical spine MRI medical image
title_sort seuneter: channel attentive u-net for instance segmentation of the cervical spine mri medical image
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9763568/
https://www.ncbi.nlm.nih.gov/pubmed/36561215
http://dx.doi.org/10.3389/fphys.2022.1081441
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