CANet: Context Aware Network for 3D Brain Glioma Segmentation

TL;DR

CANet introduces a novel context-aware deep learning approach for brain glioma segmentation that effectively incorporates tumor and surrounding tissue context, leading to improved accuracy over existing methods.

Contribution

The paper presents a new neural network architecture, CANet, which captures high-dimensional features with contextual information using feature interaction graphs and context-guided attention mechanisms.

Findings

Outperforms several state-of-the-art methods on BRATS datasets

Achieves better or competitive segmentation metrics

Demonstrates robustness across multiple datasets

Abstract

Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning. Based on deep neural networks, previous studies have shown promising technologies for brain glioma segmentation. However, these approaches lack powerful strategies to incorporate contextual information of tumor cells and their surrounding, which has been proven as a fundamental cue to deal with local ambiguity. In this work, we propose a novel approach named Context-Aware Network (CANet) for brain glioma segmentation. CANet captures high dimensional and discriminative features with contexts from both the convolutional space and feature interaction graphs. We further propose context guided attentive conditional random fields which can selectively aggregate features. We evaluate our method using publicly accessible brain glioma segmentation datasets BRATS2017, BRATS2018 and BRATS2019. The experimental results show that the proposed algorithm has better or competitive performance against several State-of-The-Art approaches under different segmentation metrics on the training and validation sets.