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

TL;DR

This paper introduces ME-Net, a multi-encoder framework for 3D brain tumor segmentation in MRI, utilizing multiple modalities and a novel loss function to improve accuracy and address voxel imbalance.

Contribution

The paper presents a novel multi-encoder architecture with a new loss function for improved 3D brain tumor segmentation from MRI modalities.

Findings

Achieved Dice scores of 0.70249, 0.88267, and 0.73864 on BraTS 2020 validation set.

Outperformed several state-of-the-art methods in tumor segmentation accuracy.

Effectively addressed voxel imbalance with the proposed Categorical Dice loss.

Abstract

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.