End-to-End Nested Multi-Attention Network for 3D Brain Tumor Segmentation

2020 ◽  
pp. 62-76
Author(s):  
Xinrui Zhuang ◽  
Yujiu Yang
Keyword(s):  
Brain Tumor ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Attention Network ◽  
End To End

scholarly journals Aggregation-and-Attention Network for brain tumor segmentation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chih-Wei Lin ◽  
Yu Hong ◽  
Jinfu Liu
Keyword(s):  
Brain Tumor ◽  
Semantic Information ◽  
Spatial Relationship ◽  
Tumor Segmentation ◽  
Computer Assisted ◽  
Brain Tumor Segmentation ◽  
Attention Network ◽  
Multi Scale ◽  
Assisted Diagnosis ◽  
The Brain

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.

ToStaGAN: An End-to-End Two-Stage Generative Adversarial Network for Brain Tumor Segmentation

2021 ◽  
Author(s):  
Yi Ding ◽  
Chao Zhang ◽  
Mingsheng Cao ◽  
Yilei Wang ◽  
Dajiang Chen ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Tumor Segmentation ◽  
Two Stage ◽  
Generative Adversarial Network ◽  
Brain Tumor Segmentation ◽  
Adversarial Network ◽  
End To End

3D asymmetric expectation‐maximization attention network for brain tumor segmentation

2021 ◽  
Author(s):  
Jianxin Zhang ◽  
Zongkang Jiang ◽  
Dongwei Liu ◽  
Qiule Sun ◽  
Yaqing Hou ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Expectation Maximization ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Attention Network
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