Cargando…

scSE-NL V-Net: A Brain Tumor Automatic Segmentation Method Based on Spatial and Channel “Squeeze-and-Excitation” Network With Non-local Block

Intracranial tumors are commonly known as brain tumors, which can be life-threatening in severe cases. Magnetic resonance imaging (MRI) is widely used in diagnosing brain tumors because of its harmless to the human body and high image resolution. Due to the heterogeneity of brain tumor height, MRI i...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhou, Juhua, Ye, Jianming, Liang, Yu, Zhao, Jialu, Wu, Yan, Luo, Siyuan, Lai, Xiaobo, Wang, Jianqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9197379/
https://www.ncbi.nlm.nih.gov/pubmed/35712454
http://dx.doi.org/10.3389/fnins.2022.916818
_version_ 1784727392782123008
author Zhou, Juhua
Ye, Jianming
Liang, Yu
Zhao, Jialu
Wu, Yan
Luo, Siyuan
Lai, Xiaobo
Wang, Jianqing
author_facet Zhou, Juhua
Ye, Jianming
Liang, Yu
Zhao, Jialu
Wu, Yan
Luo, Siyuan
Lai, Xiaobo
Wang, Jianqing
author_sort Zhou, Juhua
collection PubMed
description Intracranial tumors are commonly known as brain tumors, which can be life-threatening in severe cases. Magnetic resonance imaging (MRI) is widely used in diagnosing brain tumors because of its harmless to the human body and high image resolution. Due to the heterogeneity of brain tumor height, MRI imaging is exceptionally irregular. How to accurately and quickly segment brain tumor MRI images is still one of the hottest topics in the medical image analysis community. However, according to the brain tumor segmentation algorithms, we could find now, most segmentation algorithms still stay in two-dimensional (2D) image segmentation, which could not obtain the spatial dependence between features effectively. In this study, we propose a brain tumor automatic segmentation method called scSE-NL V-Net. We try to use three-dimensional (3D) data as the model input and process the data by 3D convolution to get some relevance between dimensions. Meanwhile, we adopt non-local block as the self-attention block, which can reduce inherent image noise interference and make up for the lack of spatial dependence due to convolution. To improve the accuracy of convolutional neural network (CNN) image recognition, we add the “Spatial and Channel Squeeze-and-Excitation” Network (scSE-Net) to V-Net. The dataset used in this paper is from the brain tumor segmentation challenge 2020 database. In the test of the official BraTS2020 verification set, the Dice similarity coefficient is 0.65, 0.82, and 0.76 for the enhanced tumor (ET), whole tumor (WT), and tumor core (TC), respectively. Thereby, our model can make an auxiliary effect on the diagnosis of brain tumors established.
format Online
Article
Text
id pubmed-9197379
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-91973792022-06-15 scSE-NL V-Net: A Brain Tumor Automatic Segmentation Method Based on Spatial and Channel “Squeeze-and-Excitation” Network With Non-local Block Zhou, Juhua Ye, Jianming Liang, Yu Zhao, Jialu Wu, Yan Luo, Siyuan Lai, Xiaobo Wang, Jianqing Front Neurosci Neuroscience Intracranial tumors are commonly known as brain tumors, which can be life-threatening in severe cases. Magnetic resonance imaging (MRI) is widely used in diagnosing brain tumors because of its harmless to the human body and high image resolution. Due to the heterogeneity of brain tumor height, MRI imaging is exceptionally irregular. How to accurately and quickly segment brain tumor MRI images is still one of the hottest topics in the medical image analysis community. However, according to the brain tumor segmentation algorithms, we could find now, most segmentation algorithms still stay in two-dimensional (2D) image segmentation, which could not obtain the spatial dependence between features effectively. In this study, we propose a brain tumor automatic segmentation method called scSE-NL V-Net. We try to use three-dimensional (3D) data as the model input and process the data by 3D convolution to get some relevance between dimensions. Meanwhile, we adopt non-local block as the self-attention block, which can reduce inherent image noise interference and make up for the lack of spatial dependence due to convolution. To improve the accuracy of convolutional neural network (CNN) image recognition, we add the “Spatial and Channel Squeeze-and-Excitation” Network (scSE-Net) to V-Net. The dataset used in this paper is from the brain tumor segmentation challenge 2020 database. In the test of the official BraTS2020 verification set, the Dice similarity coefficient is 0.65, 0.82, and 0.76 for the enhanced tumor (ET), whole tumor (WT), and tumor core (TC), respectively. Thereby, our model can make an auxiliary effect on the diagnosis of brain tumors established. Frontiers Media S.A. 2022-05-27 /pmc/articles/PMC9197379/ /pubmed/35712454 http://dx.doi.org/10.3389/fnins.2022.916818 Text en Copyright © 2022 Zhou, Ye, Liang, Zhao, Wu, Luo, Lai and Wang. 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 Neuroscience
Zhou, Juhua
Ye, Jianming
Liang, Yu
Zhao, Jialu
Wu, Yan
Luo, Siyuan
Lai, Xiaobo
Wang, Jianqing
scSE-NL V-Net: A Brain Tumor Automatic Segmentation Method Based on Spatial and Channel “Squeeze-and-Excitation” Network With Non-local Block
title scSE-NL V-Net: A Brain Tumor Automatic Segmentation Method Based on Spatial and Channel “Squeeze-and-Excitation” Network With Non-local Block
title_full scSE-NL V-Net: A Brain Tumor Automatic Segmentation Method Based on Spatial and Channel “Squeeze-and-Excitation” Network With Non-local Block
title_fullStr scSE-NL V-Net: A Brain Tumor Automatic Segmentation Method Based on Spatial and Channel “Squeeze-and-Excitation” Network With Non-local Block
title_full_unstemmed scSE-NL V-Net: A Brain Tumor Automatic Segmentation Method Based on Spatial and Channel “Squeeze-and-Excitation” Network With Non-local Block
title_short scSE-NL V-Net: A Brain Tumor Automatic Segmentation Method Based on Spatial and Channel “Squeeze-and-Excitation” Network With Non-local Block
title_sort scse-nl v-net: a brain tumor automatic segmentation method based on spatial and channel “squeeze-and-excitation” network with non-local block
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9197379/
https://www.ncbi.nlm.nih.gov/pubmed/35712454
http://dx.doi.org/10.3389/fnins.2022.916818
work_keys_str_mv AT zhoujuhua scsenlvnetabraintumorautomaticsegmentationmethodbasedonspatialandchannelsqueezeandexcitationnetworkwithnonlocalblock
AT yejianming scsenlvnetabraintumorautomaticsegmentationmethodbasedonspatialandchannelsqueezeandexcitationnetworkwithnonlocalblock
AT liangyu scsenlvnetabraintumorautomaticsegmentationmethodbasedonspatialandchannelsqueezeandexcitationnetworkwithnonlocalblock
AT zhaojialu scsenlvnetabraintumorautomaticsegmentationmethodbasedonspatialandchannelsqueezeandexcitationnetworkwithnonlocalblock
AT wuyan scsenlvnetabraintumorautomaticsegmentationmethodbasedonspatialandchannelsqueezeandexcitationnetworkwithnonlocalblock
AT luosiyuan scsenlvnetabraintumorautomaticsegmentationmethodbasedonspatialandchannelsqueezeandexcitationnetworkwithnonlocalblock
AT laixiaobo scsenlvnetabraintumorautomaticsegmentationmethodbasedonspatialandchannelsqueezeandexcitationnetworkwithnonlocalblock
AT wangjianqing scsenlvnetabraintumorautomaticsegmentationmethodbasedonspatialandchannelsqueezeandexcitationnetworkwithnonlocalblock