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3D AGSE-VNet: an automatic brain tumor MRI data segmentation framework
File | Description | Size | Format | |
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s12880-021-00728-8.pdf | Published version | 6.48 MB | Adobe PDF | View/Open |
Title: | 3D AGSE-VNet: an automatic brain tumor MRI data segmentation framework |
Authors: | Guan, X Yang, G Ye, J Yang, W Xu, X Jiang, W Lai, X |
Item Type: | Journal Article |
Abstract: | Background Glioma is the most common brain malignant tumor, with a high morbidity rate and a mortality rate of more than three percent, which seriously endangers human health. The main method of acquiring brain tumors in the clinic is MRI. Segmentation of brain tumor regions from multi-modal MRI scan images is helpful for treatment inspection, post-diagnosis monitoring, and effect evaluation of patients. However, the common operation in clinical brain tumor segmentation is still manual segmentation, lead to its time-consuming and large performance difference between different operators, a consistent and accurate automatic segmentation method is urgently needed. With the continuous development of deep learning, researchers have designed many automatic segmentation algorithms; however, there are still some problems: 1) The research of segmentation algorithm mostly stays on the 2D plane, this will reduce the accuracy of 3D image feature extraction to a certain extent. 2) MRI images have gray-scale offset fields that make it difficult to divide the contours accurately. Methods To meet the above challenges, we propose an automatic brain tumor MRI data segmentation framework which is called AGSE-VNet. In our study, the Squeeze and Excite (SE) module is added to each encoder, the Attention Guide Filter (AG) module is added to each decoder, using the channel relationship to automatically enhance the useful information in the channel to suppress the useless information, and use the attention mechanism to guide the edge information and remove the influence of irrelevant information such as noise. Results We used the BraTS2020 challenge online verification tool to evaluate our approach. The focus of verification is that the Dice scores of the whole tumor (WT), tumor core (TC) and enhanced tumor (ET) are 0.68, 0.85 and 0.70, respectively. Conclusion Although MRI images have different intensities, AGSE-VNet is not affected by the size of the tumor, and can more accurately extract the features of the three regions, it has achieved impressive results and made outstanding contributions to the clinical diagnosis and treatment of brain tumor patients. |
Issue Date: | 5-Jan-2022 |
Date of Acceptance: | 26-Jul-2021 |
URI: | http://hdl.handle.net/10044/1/90931 |
DOI: | 10.1186/s12880-021-00728-8 |
ISSN: | 1471-2342 |
Publisher: | BioMed Central |
Journal / Book Title: | BMC Medical Imaging |
Volume: | 22 |
Copyright Statement: | This paper is embargoed until publication. Once published it will be available fully open access. © The Author(s) 2021. 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/. The Creative Commons Public Domain Dedication waiver (http://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. |
Sponsor/Funder: | European Research Council Horizon 2020 |
Funder's Grant Number: | H2020-SC1-FA-DTS-2019-1 952172 |
Keywords: | Automatic segmentation Brain tumor Deep learning Magnetic resonance imaging VNet cs.AI cs.AI cs.CV cs.LG 1103 Clinical Sciences Nuclear Medicine & Medical Imaging |
Publication Status: | Published |
Article Number: | ARTN 6 |
Appears in Collections: | National Heart and Lung Institute Faculty of Medicine |
This item is licensed under a Creative Commons License