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Fig 1.

Technical Roadmap.

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Fig 2.

ASPP Basic Structure.

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Fig 3.

Basic structure of MFAB module.

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Fig 4.

Improve the basic structure of 3D U-Net network.

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Table 1.

Change analysis of loss function before and after improvement of glioma segmentation model.

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Table 2.

Analysis of Changes in Loss Function under Different Levels of Interference before and after Improvement.

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Table 3.

Results of Jaccard Index Index under Different DataSets.

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Table 4.

Performance Comparison and Analysis of Various Network Models.

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Table 5.

Analysis of the influence of the selection of loss function on the segmentation accuracy of glioma.

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Fig 5.

Original 3D-MRI brain glioma case sample data (a) The 1st 3D-MRI glioma sample (b) The 2nd case of 3D-MRI glioma (c) The 3rd case of 3D-MRI glioma (d) The 4th case of 3D-MRI glioma (e) The 5th case of 3D-MRI glioma (f) The 6th case of 3D-MRI glioma.

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Fig 6.

Sample labeling results of 3D-MRI glioma cases (a) The 1st 3D-MRI glioma sample (b) The 2nd case of 3D-MRI glioma (c) The 3rd case of 3D-MRI glioma (d) The 4th case of 3D-MRI glioma (e) The 5th case of 3D-MRI glioma (f) The 6th case of 3D-MRI glioma.

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Fig 7.

The segmentation results of 3D-MRI glioma case samples using the method described in this article (a) The 1st 3D-MRI glioma sample (b) The 2nd case of 3D-MRI glioma (c) The 3rd case of 3D-MRI glioma (d) The 4th case of 3D-MRI glioma (e) The 5th case of 3D-MRI glioma (f) The 6th case of 3D-MRI glioma.

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Fig 8.

Sample segmentation results of 3D-MRI glioma cases using comparative methods (a) The 1st 3D-MRI glioma sample (b) The 2nd case of 3D-MRI glioma (c) The 3rd case of 3D-MRI glioma (d) The 4th case of 3D-MRI glioma (e) The 5th case of 3D-MRI glioma (f) The 6th case of 3D-MRI glioma.

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Fig 9.

HD95 distance results.

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Fig 10.

Segmentation Error Results.

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Fig 11.

Linear time complexity results.

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