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Aggregation-and-Attention Network for brain tumor segmentation.
- Source :
- BMC Medical Imaging; 7/9/2021, Vol. 21 Issue 1, p1-12, 12p
- Publication Year :
- 2021
-
Abstract
- Background: Glioma is a malignant brain tumor; its location is complex and is difficult to remove surgically. To diagnosis the brain tumor, doctors can precisely diagnose and localize the disease using medical images. However, the computer-assisted diagnosis for the brain tumor diagnosis is still the problem because the rough segmentation of the brain tumor makes the internal grade of the tumor incorrect. Methods: In this paper, we proposed an Aggregation-and-Attention Network for brain tumor segmentation. The proposed network takes the U-Net as the backbone, aggregates multi-scale semantic information, and focuses on crucial information to perform brain tumor segmentation. To this end, we proposed an enhanced down-sampling module and Up-Sampling Layer to compensate for the information loss. The multi-scale connection module is to construct the multi-receptive semantic fusion between encoder and decoder. Furthermore, we designed a dual-attention fusion module that can extract and enhance the spatial relationship of magnetic resonance imaging and applied the strategy of deep supervision in different parts of the proposed network. Results: Experimental results show that the performance of the proposed framework is the best on the BraTS2020 dataset, compared with the-state-of-art networks. The performance of the proposed framework surpasses all the comparison networks, and its average accuracies of the four indexes are 0.860, 0.885, 0.932, and 1.2325, respectively. Conclusions: The framework and modules of the proposed framework are scientific and practical, which can extract and aggregate useful semantic information and enhance the ability of glioma segmentation. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14712342
- Volume :
- 21
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- BMC Medical Imaging
- Publication Type :
- Academic Journal
- Accession number :
- 151331969
- Full Text :
- https://doi.org/10.1186/s12880-021-00639-8