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CANet: Context Aware Network for Brain Glioma Segmentation

Liu, Zhihua, Tong, Lei, Chen, Long, Zhou, Feixiang, Jiang, Zheheng, Zhang, Qianni, Wang, Yinhai, Shan, Caifeng, Li, Ling, Zhou, Huiyu and others. (2021) CANet: Context Aware Network for Brain Glioma Segmentation. IEEE Transactions on Medical Imaging, . ISSN 0278-0062. E-ISSN 1558-254X. (doi:10.1109/TMI.2021.3065918) (KAR id:84603)

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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.

Item Type: Article
DOI/Identification number: 10.1109/TMI.2021.3065918
Subjects: Q Science
Q Science > Q Science (General)
Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Caroline Li
Date Deposited: 25 Mar 2021 22:11 UTC
Last Modified: 09 Dec 2022 00:05 UTC
Resource URI: (The current URI for this page, for reference purposes)
Li, Ling:
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