CANet: Context Aware Network for 3D Brain Glioma Segmentation
This work addresses brain glioma segmentation for medical diagnosis and planning, but it appears incremental as it builds on existing deep learning approaches with context-aware enhancements.
The paper tackles automated segmentation of brain glioma by proposing CANet, a context-aware network that incorporates contextual information from convolutional space and feature interaction graphs, achieving better or competitive performance against state-of-the-art methods on BRATS datasets.
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.