论文标题
类别指导注意力网络用于MRI中的脑肿瘤分割
Category Guided Attention Network for Brain Tumor Segmentation in MRI
论文作者
论文摘要
目的：磁共振成像（MRI）已被广泛用于分析和诊断脑疾病。准确和自动的脑肿瘤分割对于放射治疗至关重要。然而，肿瘤区域中的低组织对比使其成为一项艰巨的任务。请注意：我们提出了一个新型的分割网络。在此模型中，我们根据注意力机制设计了一个有监督的注意模块（SAM），该模块可以在功能地图中捕获更准确和稳定的长距离依赖性，而不会引入大量计算成本。此外，我们通过汇总同一类别的像素来重建特征图，提出了一种内部更新方法，以重建特征图。主要结果：BRATS 2019数据集的实验结果表明，该方法在分割性能和计算复杂性中的最新算法优于最先进的算法。意义：CGA U-NET可以通过使用SAM模块有效地捕获MRI图像中的全局语义信息，同时大大降低了计算成本。代码可在https://github.com/delugewalker/cga-u-net上找到。
Objective: Magnetic resonance imaging (MRI) has been widely used for the analysis and diagnosis of brain diseases. Accurate and automatic brain tumor segmentation is of paramount importance for radiation treatment. However, low tissue contrast in tumor regions makes it a challenging task.Approach: We propose a novel segmentation network named Category Guided Attention U-Net (CGA U-Net). In this model, we design a Supervised Attention Module (SAM) based on the attention mechanism, which can capture more accurate and stable long-range dependency in feature maps without introducing much computational cost. Moreover, we propose an intra-class update approach to reconstruct feature maps by aggregating pixels of the same category. Main results: Experimental results on the BraTS 2019 datasets show that the proposed method outperformers the state-of-the-art algorithms in both segmentation performance and computational complexity. Significance: The CGA U-Net can effectively capture the global semantic information in the MRI image by using the SAM module, while significantly reducing the computational cost. Code is available at https://github.com/delugewalker/CGA-U-Net.
