Fan Chaodong, Zeng Zhenhuan, Xiao Leyi, Qu Xilong
School of Computer Science and Technology, Hainan University, Haikou 570228, China.
School of Computer Science, Xiangtan University, Xiangtan 411100, China.
Pattern Recognit. 2022 Dec;132:108963. doi: 10.1016/j.patcog.2022.108963. Epub 2022 Aug 8.
In early 2020, the global spread of the COVID-19 has presented the world with a serious health crisis. Due to the large number of infected patients, automatic segmentation of lung infections using computed tomography (CT) images has great potential to enhance traditional medical strategies. However, the segmentation of infected regions in CT slices still faces many challenges. Specially, the most core problem is the high variability of infection characteristics and the low contrast between the infected and the normal regions. This problem leads to fuzzy regions in lung CT segmentation. To address this problem, we have designed a novel global feature network(GFNet) for COVID-19 lung infections: VGG16 as backbone, we design a Edge-guidance module(Eg) that fuses the features of each layer. First, features are extracted by reverse attention module and Eg is combined with it. This series of steps enables each layer to fully extract boundary details that are difficult to be noticed by previous models, thus solving the fuzzy problem of infected regions. The multi-layer output features are fused into the final output to finally achieve automatic and accurate segmentation of infected areas. We compared the traditional medical segmentation networks, UNet, UNet++, the latest model Inf-Net, and methods of few shot learning field. Experiments show that our model is superior to the above models in Dice, Sensitivity, Specificity and other evaluation metrics, and our segmentation results are clear and accurate from the visual effect, which proves the effectiveness of GFNet. In addition, we verify the generalization ability of GFNet on another "never seen" dataset, and the results prove that our model still has better generalization ability than the above model. Our code has been shared at https://github.com/zengzhenhuan/GFNet.
2020年初，新型冠状病毒肺炎（COVID-19）的全球传播给世界带来了严重的健康危机。由于感染患者数量众多，利用计算机断层扫描（CT）图像对肺部感染进行自动分割对于改进传统医疗策略具有巨大潜力。然而，CT切片中感染区域的分割仍面临许多挑战。特别是，最核心的问题是感染特征的高度变异性以及感染区域与正常区域之间的低对比度。这个问题导致肺部CT分割中的区域模糊。为了解决这个问题，我们设计了一种用于COVID-19肺部感染的新型全局特征网络（GFNet）：以VGG16为骨干网络，我们设计了一个边缘引导模块（Eg），它融合了每一层的特征。首先，通过反向注意力模块提取特征，并将Eg与之结合。这一系列步骤使每一层都能充分提取先前模型难以注意到的边界细节，从而解决感染区域的模糊问题。将多层输出特征融合到最终输出中，最终实现感染区域的自动准确分割。我们将传统医学分割网络UNet、UNet++、最新模型Inf-Net以及少样本学习领域的方法进行了比较。实验表明，我们的模型在Dice、灵敏度、特异性等评估指标上优于上述模型，并且从视觉效果来看，我们的分割结果清晰准确，证明了GFNet的有效性。此外，我们在另一个“未见”数据集上验证了GFNet的泛化能力，结果证明我们的模型仍然比上述模型具有更好的泛化能力。我们的代码已在https://github.com/zengzhenhuan/GFNet上共享。
