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Brain tumor image segmentation based on improved FPN
PURPOSE: Automatic segmentation of brain tumors by deep learning algorithm is one of the research hotspots in the field of medical image segmentation. An improved FPN network for brain tumor segmentation is proposed to improve the segmentation effect of brain tumor. MATERIALS AND METHODS: Aiming at...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10617057/ https://www.ncbi.nlm.nih.gov/pubmed/37904116 http://dx.doi.org/10.1186/s12880-023-01131-1 |
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author | Sun, Haitao Yang, Shuai Chen, Lijuan Liao, Pingyan Liu, Xiangping Liu, Ying Wang, Ning |
author_facet | Sun, Haitao Yang, Shuai Chen, Lijuan Liao, Pingyan Liu, Xiangping Liu, Ying Wang, Ning |
author_sort | Sun, Haitao |
collection | PubMed |
description | PURPOSE: Automatic segmentation of brain tumors by deep learning algorithm is one of the research hotspots in the field of medical image segmentation. An improved FPN network for brain tumor segmentation is proposed to improve the segmentation effect of brain tumor. MATERIALS AND METHODS: Aiming at the problem that the traditional full convolutional neural network (FCN) has weak processing ability, which leads to the loss of details in tumor segmentation, this paper proposes a brain tumor image segmentation method based on the improved feature pyramid networks (FPN) convolutional neural network. In order to improve the segmentation effect of brain tumors, we improved the model, introduced the FPN structure into the U-Net structure, captured the context multi-scale information by using the different scale information in the U-Net model and the multi receptive field high-level features in the FPN convolutional neural network, and improved the adaptability of the model to different scale features. RESULTS: Performance evaluation indicators show that the proposed improved FPN model has 99.1% accuracy, 92% DICE rating and 86% Jaccard index. The performance of the proposed method outperforms other segmentation models in each metric. In addition, the schematic diagram of the segmentation results shows that the segmentation results of our algorithm are closer to the ground truth, showing more brain tumour details, while the segmentation results of other algorithms are smoother. CONCLUSIONS: The experimental results show that this method can effectively segment brain tumor regions and has certain generalization, and the segmentation effect is better than other networks. It has positive significance for clinical diagnosis of brain tumors. |
format | Online Article Text |
id | pubmed-10617057 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106170572023-11-01 Brain tumor image segmentation based on improved FPN Sun, Haitao Yang, Shuai Chen, Lijuan Liao, Pingyan Liu, Xiangping Liu, Ying Wang, Ning BMC Med Imaging Research PURPOSE: Automatic segmentation of brain tumors by deep learning algorithm is one of the research hotspots in the field of medical image segmentation. An improved FPN network for brain tumor segmentation is proposed to improve the segmentation effect of brain tumor. MATERIALS AND METHODS: Aiming at the problem that the traditional full convolutional neural network (FCN) has weak processing ability, which leads to the loss of details in tumor segmentation, this paper proposes a brain tumor image segmentation method based on the improved feature pyramid networks (FPN) convolutional neural network. In order to improve the segmentation effect of brain tumors, we improved the model, introduced the FPN structure into the U-Net structure, captured the context multi-scale information by using the different scale information in the U-Net model and the multi receptive field high-level features in the FPN convolutional neural network, and improved the adaptability of the model to different scale features. RESULTS: Performance evaluation indicators show that the proposed improved FPN model has 99.1% accuracy, 92% DICE rating and 86% Jaccard index. The performance of the proposed method outperforms other segmentation models in each metric. In addition, the schematic diagram of the segmentation results shows that the segmentation results of our algorithm are closer to the ground truth, showing more brain tumour details, while the segmentation results of other algorithms are smoother. CONCLUSIONS: The experimental results show that this method can effectively segment brain tumor regions and has certain generalization, and the segmentation effect is better than other networks. It has positive significance for clinical diagnosis of brain tumors. BioMed Central 2023-10-30 /pmc/articles/PMC10617057/ /pubmed/37904116 http://dx.doi.org/10.1186/s12880-023-01131-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Sun, Haitao Yang, Shuai Chen, Lijuan Liao, Pingyan Liu, Xiangping Liu, Ying Wang, Ning Brain tumor image segmentation based on improved FPN |
title | Brain tumor image segmentation based on improved FPN |
title_full | Brain tumor image segmentation based on improved FPN |
title_fullStr | Brain tumor image segmentation based on improved FPN |
title_full_unstemmed | Brain tumor image segmentation based on improved FPN |
title_short | Brain tumor image segmentation based on improved FPN |
title_sort | brain tumor image segmentation based on improved fpn |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10617057/ https://www.ncbi.nlm.nih.gov/pubmed/37904116 http://dx.doi.org/10.1186/s12880-023-01131-1 |
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