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

by   Wenbo Zhang, et al.

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.


page 4

page 9

page 12

page 19

page 20


Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans

Gliomas are one of the most frequent brain tumors and are classified int...

Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis

Cancer of the brain is deadly and requires careful surgical segmentation...

Perturb-and-MPM: Quantifying Segmentation Uncertainty in Dense Multi-Label CRFs

This paper proposes a novel approach for uncertainty quantification in d...

A Neural Ordinary Differential Equation Model for Visualizing Deep Neural Network Behaviors in Multi-Parametric MRI based Glioma Segmentation

Purpose: To develop a neural ordinary differential equation (ODE) model ...

DeepDefacer: Automatic Removal of Facial Features via U-Net Image Segmentation

Recent advancements in the field of magnetic resonance imaging (MRI) hav...

MAG-Net: Mutli-task attention guided network for brain tumor segmentation and classification

Brain tumor is the most common and deadliest disease that can be found i...

Please sign up or login with your details

Forgot password? Click here to reset