Academic Journal
3D AGSE-VNet: an automatic brain tumor MRI data segmentation framework
| العنوان: | 3D AGSE-VNet: an automatic brain tumor MRI data segmentation framework |
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| المؤلفون: | Guan, Xi, Yang, Guang, Ye, Jianming, Yang, Weiji, Xu, Xiaomei, Jiang, Weiwei, Lai, Xiaobo |
| المساهمون: | Natural Science Foundation of Zhejiang Province, Medical and Health Science and Technology Plan of Zhejiang Province, Teacher Professional Development Project of Domestic Visiting Scholar in Colleges and Universities of Zhejiang Province of China, H2020 European Research Council |
| المصدر: | BMC Medical Imaging ; volume 22, issue 1 ; ISSN 1471-2342 |
| بيانات النشر: | Springer Science and Business Media LLC |
| سنة النشر: | 2022 |
| الوصف: | 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, tumor core and enhanced tumor 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 ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| DOI: | 10.1186/s12880-021-00728-8 |
| DOI: | 10.1186/s12880-021-00728-8.pdf |
| DOI: | 10.1186/s12880-021-00728-8/fulltext.html |
| الاتاحة: | http://dx.doi.org/10.1186/s12880-021-00728-8 https://link.springer.com/content/pdf/10.1186/s12880-021-00728-8.pdf https://link.springer.com/article/10.1186/s12880-021-00728-8/fulltext.html |
| Rights: | https://creativecommons.org/licenses/by/4.0 ; https://creativecommons.org/licenses/by/4.0 |
| رقم الانضمام: | edsbas.C6DC8136 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1186/s12880-021-00728-8 |
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