Cargando…
Feature interaction network based on hierarchical decoupled convolution for 3D medical image segmentation
Manual image segmentation consumes time. An automatic and accurate method to segment multimodal brain tumors using context information rich three-dimensional medical images that can be used for clinical treatment decisions and surgical planning is required. However, it is a challenge to use deep lea...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10343050/ https://www.ncbi.nlm.nih.gov/pubmed/37440581 http://dx.doi.org/10.1371/journal.pone.0288658 |
_version_ | 1785072645841092608 |
---|---|
author | Shen, Longfeng Zhang, Yingjie Wang, Qiong Qin, Fenglan Sun, Dengdi Min, Hai Meng, Qianqian Xu, Chengzhen Zhao, Wei Song, Xin |
author_facet | Shen, Longfeng Zhang, Yingjie Wang, Qiong Qin, Fenglan Sun, Dengdi Min, Hai Meng, Qianqian Xu, Chengzhen Zhao, Wei Song, Xin |
author_sort | Shen, Longfeng |
collection | PubMed |
description | Manual image segmentation consumes time. An automatic and accurate method to segment multimodal brain tumors using context information rich three-dimensional medical images that can be used for clinical treatment decisions and surgical planning is required. However, it is a challenge to use deep learning to achieve accurate segmentation of medical images due to the diversity of tumors and the complex boundary interactions between sub-regions while limited computing resources hinder the construction of efficient neural networks. We propose a feature fusion module based on a hierarchical decoupling convolution network and an attention mechanism to improve the performance of network segmentation. We replaced the skip connections of U-shaped networks with a feature fusion module to solve the category imbalance problem, thus contributing to the segmentation of more complicated medical images. We introduced a global attention mechanism to further integrate the features learned by the encoder and explore the context information. The proposed method was evaluated for enhance tumor, whole tumor, and tumor core, achieving Dice similarity coefficient metrics of 0.775, 0.900, and 0.827, respectively, on the BraTS 2019 dataset and 0.800, 0.902, and 0.841, respectively on the BraTS 2018 dataset. The results show that our proposed method is inherently general and is a powerful tool for brain tumor image studies. Our code is available at: https://github.com/WSake/Feature-interaction-network-based-on-Hierarchical-Decoupled-Convolution. |
format | Online Article Text |
id | pubmed-10343050 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-103430502023-07-14 Feature interaction network based on hierarchical decoupled convolution for 3D medical image segmentation Shen, Longfeng Zhang, Yingjie Wang, Qiong Qin, Fenglan Sun, Dengdi Min, Hai Meng, Qianqian Xu, Chengzhen Zhao, Wei Song, Xin PLoS One Research Article Manual image segmentation consumes time. An automatic and accurate method to segment multimodal brain tumors using context information rich three-dimensional medical images that can be used for clinical treatment decisions and surgical planning is required. However, it is a challenge to use deep learning to achieve accurate segmentation of medical images due to the diversity of tumors and the complex boundary interactions between sub-regions while limited computing resources hinder the construction of efficient neural networks. We propose a feature fusion module based on a hierarchical decoupling convolution network and an attention mechanism to improve the performance of network segmentation. We replaced the skip connections of U-shaped networks with a feature fusion module to solve the category imbalance problem, thus contributing to the segmentation of more complicated medical images. We introduced a global attention mechanism to further integrate the features learned by the encoder and explore the context information. The proposed method was evaluated for enhance tumor, whole tumor, and tumor core, achieving Dice similarity coefficient metrics of 0.775, 0.900, and 0.827, respectively, on the BraTS 2019 dataset and 0.800, 0.902, and 0.841, respectively on the BraTS 2018 dataset. The results show that our proposed method is inherently general and is a powerful tool for brain tumor image studies. Our code is available at: https://github.com/WSake/Feature-interaction-network-based-on-Hierarchical-Decoupled-Convolution. Public Library of Science 2023-07-13 /pmc/articles/PMC10343050/ /pubmed/37440581 http://dx.doi.org/10.1371/journal.pone.0288658 Text en © 2023 Shen et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Shen, Longfeng Zhang, Yingjie Wang, Qiong Qin, Fenglan Sun, Dengdi Min, Hai Meng, Qianqian Xu, Chengzhen Zhao, Wei Song, Xin Feature interaction network based on hierarchical decoupled convolution for 3D medical image segmentation |
title | Feature interaction network based on hierarchical decoupled convolution for 3D medical image segmentation |
title_full | Feature interaction network based on hierarchical decoupled convolution for 3D medical image segmentation |
title_fullStr | Feature interaction network based on hierarchical decoupled convolution for 3D medical image segmentation |
title_full_unstemmed | Feature interaction network based on hierarchical decoupled convolution for 3D medical image segmentation |
title_short | Feature interaction network based on hierarchical decoupled convolution for 3D medical image segmentation |
title_sort | feature interaction network based on hierarchical decoupled convolution for 3d medical image segmentation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10343050/ https://www.ncbi.nlm.nih.gov/pubmed/37440581 http://dx.doi.org/10.1371/journal.pone.0288658 |
work_keys_str_mv | AT shenlongfeng featureinteractionnetworkbasedonhierarchicaldecoupledconvolutionfor3dmedicalimagesegmentation AT zhangyingjie featureinteractionnetworkbasedonhierarchicaldecoupledconvolutionfor3dmedicalimagesegmentation AT wangqiong featureinteractionnetworkbasedonhierarchicaldecoupledconvolutionfor3dmedicalimagesegmentation AT qinfenglan featureinteractionnetworkbasedonhierarchicaldecoupledconvolutionfor3dmedicalimagesegmentation AT sundengdi featureinteractionnetworkbasedonhierarchicaldecoupledconvolutionfor3dmedicalimagesegmentation AT minhai featureinteractionnetworkbasedonhierarchicaldecoupledconvolutionfor3dmedicalimagesegmentation AT mengqianqian featureinteractionnetworkbasedonhierarchicaldecoupledconvolutionfor3dmedicalimagesegmentation AT xuchengzhen featureinteractionnetworkbasedonhierarchicaldecoupledconvolutionfor3dmedicalimagesegmentation AT zhaowei featureinteractionnetworkbasedonhierarchicaldecoupledconvolutionfor3dmedicalimagesegmentation AT songxin featureinteractionnetworkbasedonhierarchicaldecoupledconvolutionfor3dmedicalimagesegmentation |