基于深度学习和 CT 影像的新型冠状病毒肺炎病灶分割
基于深度学习和 CT 影像的新型冠状病毒肺炎病灶分割
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Segmentation of COVID-19 Lesions Based on Deep Learning and CT Images
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    摘要:

    胸部 CT 图像中新型冠状病毒肺炎(COVID-19)病灶的准确分割可以为诊断提供助力。新型冠 状病毒肺炎在 CT 影像上的表现包括磨玻璃影、实变、胸腔积液病灶等,这些征象的纹理、大小和位置 变化较大。该研究提出的深度神经网络 RCB-UNet++,可以用于新型冠状病毒肺炎在 CT 影像上的分割 问题。该网络在 UNet++网络的基础上,增加了残差模块和卷积块注意力模块。此架构能有效地提取底 层的纹理信息和高层的语义信息,并基于注意力机制优化模型效果。该研究所提出的 RCB-UNet++模 型经过在 45 例样本上的训练后,在 50 例测试集上的 Dice 系数达到了 0.715,且敏感性和特异性分别达 到了 0.754 和 0.952,超过基于同样数据划分的其他已发表的深度学习模型。这表明所提出的算法有效 地提高了分割效果,具有从 CT 图像中全自动分割新型冠状病毒肺炎病灶的潜力。

    Abstract:

    Accurate segmentation of COVID-19 pneumonia lesions on chest CT images can facilitate the diagnosis of pneumonia. The CT image finds of which contained the ground-glass opacity, consolidation, pleural effusion, etc. This study proposed a deep neural network RCB-UNet++ for the segmentation of COVID-19 pneumonia lesions in CT images, which exhibit large variations in texture, size and location. The model was built on top of the UNet++ network with an extra residual module and an attention module. This architecture is able to effectively extract low-level texture features and high-level semantic information, thus improving the model performance. The RCB-UNet++ model was trained on 45 samples and tested by another 50 cases. Finally, it achieved a Dice coefficient of 0.715, a sensitivity and specificity of 0.754 and 0.952, outperforming other designed models on the same dataset. The results demonstrate that the proposed algorithm improves the segmentation performance and has potential in fully automatic segmentation of COVID-19 pneumonia lesions on CT images.

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引文格式
毛 丽,李秀丽.基于深度学习和 CT 影像的新型冠状病毒肺炎病灶分割 [J].集成技术,2020,9(6):40-47

Citing format
MAO Li, LI Xiuli. Segmentation of COVID-19 Lesions Based on Deep Learning and CT Images[J]. Journal of Integration Technology,2020,9(6):40-47

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  • 在线发布日期: 2020-11-24
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