Wang Jingjing, Yu Zishu, Luan Zhenye, Ren Jinwen, Zhao Yanhua, Yu Gang
College of Physics and Electronics Science, Shandong Normal University, Jinan, China.
Obstetrics and Gynecology, Tengzhou Xigang Central Health Center, Tengzhou, China.
Front Oncol. 2022 Mar 2;12:805263. doi: 10.3389/fonc.2022.805263. eCollection 2022.
Due to the high heterogeneity of brain tumors, automatic segmentation of brain tumors remains a challenging task. In this paper, we propose RDAU-Net by adding dilated feature pyramid blocks with 3D CBAM blocks and inserting 3D CBAM blocks after skip-connection layers. Moreover, a CBAM with channel attention and spatial attention facilitates the combination of more expressive feature information, thereby leading to more efficient extraction of contextual information from images of various scales. The performance was evaluated on the Multimodal Brain Tumor Segmentation (BraTS) challenge data. Experimental results show that RDAU-Net achieves state-of-the-art performance. The Dice coefficient for WT on the BraTS 2019 dataset exceeded the baseline value by 9.2%.
由于脑肿瘤的高度异质性，脑肿瘤的自动分割仍然是一项具有挑战性的任务。在本文中，我们通过添加带有3D CBAM模块的扩张特征金字塔块并在跳跃连接层之后插入3D CBAM模块来提出RDAU-Net。此外，具有通道注意力和空间注意力的CBAM有助于组合更具表现力的特征信息，从而更有效地从各种尺度的图像中提取上下文信息。在多模态脑肿瘤分割（BraTS）挑战数据上对性能进行了评估。实验结果表明，RDAU-Net实现了最优性能。在BraTS 2019数据集上，WT的Dice系数比基线值高出9.2%。
