mlsanityData to 14 Oct 2026

3D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Framework

arXiv 2107.12046 ↗First posted 26 Jul 2021cs.AI cs.CV cs.LGcited by 9

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Abstract (arXiv)

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. 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.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 14 of its 33 references that are in the map. First posted on arXiv 26 Jul 2021.

Builds on

14 of 33 references.

References · 14

  1. 2017Squeeze-and-Excitation Networkscited by 4,789
  2. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  3. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 511
  4. 20183D MRI brain tumor segmentation using autoencoder regularizationcited by 328
  5. 2019MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentationcited by 195
  6. 2015Brain Tumor Segmentation with Deep Neural Networkscited by 137
  7. 2017Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networkscited by 42
  8. 2017A deep learning model integrating FCNNs and CRFs for brain tumor segmentationcited by 35
  9. 2017Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networkscited by 33
  10. 2018Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Featurescited by 33
  11. 2019Attention Guided Network for Retinal Image Segmentationcited by 32
  12. 2019One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentationcited by 25
2 more
  1. 2017Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Networkcited by 7
  2. 2019`Project & Excite' Modules for Segmentation of Volumetric Medical Scanscited by 7

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Cited by year

Citing papers per year: 2022: 5; 2023: 1; 2024: 2; 2025: 1.

9 papers.

  1. 2022Large-Kernel Attention for 3D Medical Image Segmentationcited by 7
  2. 2024A Weakly Supervised and Globally Explainable Learning Framework for Brain Tumor Segmentationcited by 6
  3. 2022Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directionscited by 5
  4. 2022Multiple Sclerosis Lesions Segmentation using Attention-Based CNNs in FLAIR Imagescited by 2
  5. 2022CTVR-EHO TDA-IPH Topological Optimized Convolutional Visual Recurrent Network for Brain Tumor Segmentation and Classificationcited by 0
  6. 2022WSC-Trans: A 3D network model for automatic multi-structural segmentation of temporal bone CTcited by 0
  7. 2023A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentationcited by 0
  8. 2024Multimodal 3D Brain Tumor Segmentation with Adversarial Training and Conditional Random Fieldcited by 0
  9. 2025ReFRM3D: A Radiomics-enhanced Fused Residual Multiparametric 3D Network with Multi-Scale Feature Fusion for Glioma Characterizationcited by 0