# ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation

arXiv: https://arxiv.org/abs/2203.11213 · HTML: https://mlsanity.com/paper/2203.11213/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 21 Mar 2022.

## Builds on (most cited first, 17 of 17)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [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)
- [A deep learning model integrating FCNNs and CRFs for brain tumor segmentation](https://mlsanity.com/paper/1702.04528/) (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)
- [One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentation](https://mlsanity.com/paper/1906.01796/) (2019)
- [Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation](https://mlsanity.com/paper/1506.07452/) (2015)
- [MRI Tumor Segmentation with Densely Connected 3D CNN](https://mlsanity.com/paper/1802.02427/) (2018)
- [Glioma Segmentation with Cascaded Unet](https://mlsanity.com/paper/1810.04008/) (2018)
- [Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network](https://mlsanity.com/paper/1708.00377/) (2017)
- [An Iterative Convolutional Neural Network Algorithm Improves Electron Microscopy Image Segmentation](https://mlsanity.com/paper/1506.05849/) (2015)

## Built on by (most cited first, 4 of 4)

- [Large-Kernel Attention for 3D Medical Image Segmentation](https://mlsanity.com/paper/2207.11225/) (2022)
- [A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation](https://mlsanity.com/paper/2207.14134/) (2022)
- [GBT-SAM: A Parameter-Efficient Depth-Aware Model for Generalizable Brain tumour Segmentation on mp-MRI](https://mlsanity.com/paper/2503.04325/) (2025)
- [Adaptive Dual Residual U-Net with Attention Gate and Multiscale Spatial Attention Mechanisms (ADRUwAMS)](https://mlsanity.com/paper/2604.08893/) (2026)

## Abstract (arXiv)

Glioma is the most common and aggressive brain tumor. Magnetic resonance imaging (MRI) plays a vital role to evaluate tumors for the arrangement of tumor surgery and the treatment of subsequent procedures. However, the manual segmentation of the MRI image is strenuous, which limits its clinical application. With the development of deep learning, a large number of automatic segmentation methods have been developed, but most of them stay in 2D images, which leads to subpar performance. Moreover, the serious voxel imbalance between the brain tumor and the background as well as the different sizes and locations of the brain tumor makes the segmentation of 3D images a challenging problem. Aiming at segmenting 3D MRI, we propose a model for brain tumor segmentation with multiple encoders. The structure contains four encoders and one decoder. The four encoders correspond to the four modalities of the MRI image, perform one-to-one feature extraction, and then merge the feature maps of the four modalities into the decoder. This method reduces the difficulty of feature extraction and greatly improves model performance. We also introduced a new loss function named "Categorical Dice", and set different weights for different segmented regions at the same time, which solved the problem of voxel imbalance. We evaluated our approach using the online BraTS 2020 Challenge verification. Our proposed method can achieve promising results in the validation set compared to the state-of-the-art approaches with Dice scores of 0.70249, 0.88267, and 0.73864 for the intact tumor, tumor core, and enhanced tumor, respectively.