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

arXiv: https://arxiv.org/abs/2107.12046 · HTML: https://mlsanity.com/paper/2107.12046/

## 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 (most cited first, 14 of 14)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [3D MRI brain tumor segmentation using autoencoder regularization](https://mlsanity.com/paper/1810.11654/) (2018)
- [MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation](https://mlsanity.com/paper/1902.04049/) (2019)
- [Brain Tumor Segmentation with Deep Neural Networks](https://mlsanity.com/paper/1505.03540/) (2015)
- [Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks](https://mlsanity.com/paper/1705.03820/) (2017)
- [A deep learning model integrating FCNNs and CRFs for brain tumor segmentation](https://mlsanity.com/paper/1702.04528/) (2017)
- [Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks](https://mlsanity.com/paper/1709.00382/) (2017)
- [Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features](https://mlsanity.com/paper/1812.01049/) (2018)
- [Attention Guided Network for Retinal Image Segmentation](https://mlsanity.com/paper/1907.12930/) (2019)
- [One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentation](https://mlsanity.com/paper/1906.01796/) (2019)
- [Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network](https://mlsanity.com/paper/1708.00377/) (2017)
- [`Project & Excite' Modules for Segmentation of Volumetric Medical Scans](https://mlsanity.com/paper/1906.04649/) (2019)

## Built on by (most cited first, 9 of 9)

- [Large-Kernel Attention for 3D Medical Image Segmentation](https://mlsanity.com/paper/2207.11225/) (2022)
- [A Weakly Supervised and Globally Explainable Learning Framework for Brain Tumor Segmentation](https://mlsanity.com/paper/2408.01191/) (2024)
- [Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions](https://mlsanity.com/paper/2209.10307/) (2022)
- [Multiple Sclerosis Lesions Segmentation using Attention-Based CNNs in FLAIR Images](https://mlsanity.com/paper/2201.01832/) (2022)
- [CTVR-EHO TDA-IPH Topological Optimized Convolutional Visual Recurrent Network for Brain Tumor Segmentation and Classification](https://mlsanity.com/paper/2207.13021/) (2022)
- [WSC-Trans: A 3D network model for automatic multi-structural segmentation of temporal bone CT](https://mlsanity.com/paper/2211.07143/) (2022)
- [A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation](https://mlsanity.com/paper/2307.08048/) (2023)
- [Multimodal 3D Brain Tumor Segmentation with Adversarial Training and Conditional Random Field](https://mlsanity.com/paper/2411.14418/) (2024)
- [ReFRM3D: A Radiomics-enhanced Fused Residual Multiparametric 3D Network with Multi-Scale Feature Fusion for Glioma Characterization](https://mlsanity.com/paper/2512.22570/) (2025)

## 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.