Precise segmentation of lung parenchyma is essential for effective analysis of the lung. Due to the obvious contrast and large regional area compared to other tissues in the chest, lung tissue is less difficult to segment. Special attention to details of lung segmentation is also needed. To improvethe quality and speed of segmentation of lung parenchyma based on computed tomography (CT) or computed tomography angiography (CTA) images, the 4th International Symposium on Image Computing and Digital Medicine (ISICDM 2020) provides interesting and valuable research ideas and approaches. For the work of lung parenchyma segmentation, 9 of the 12 participating teams used the U-Net network or its modified forms, and others used the methods to improve the segmentation accuracy include attention mechanism, multi-scale feature information fusion. Among them, U-Net achieves the best results including that the final dice coefficient of CT segmentation is 0.991 and the final dice coefficient of CTA segmentation is 0.984. In addition, attention U-Net and nnU-Net network also performs well. In this review paper, the methods chosen by 12 teams from different research groups are evaluated and their segmentation results are analyzed for the study and references to those involved.

中文翻译：

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基于深度学习方法的肺实质分割综述

肺实质的精确分割对于有效分析肺至关重要。由于与胸部其他组织相比具有明显的对比度和较大的区域面积，肺组织的分割难度较低。还需要特别注意肺分割的细节。为提高基于计算机断层扫描（CT）或计算机断层扫描血管造影（CTA）图像的肺实质分割质量和速度，第四届图像计算与数字医学国际研讨会（ISICDM 2020）提供了有趣且有价值的研究思路和方法。对于肺实质分割的工作，12个参赛团队中有9个使用了U-Net网络或其修改形式，其他的则使用了注意力机制、多尺度特征信息融合等提高分割精度的方法。他们之中，U-Net取得了最好的结果，包括CT分割的最终骰子系数为0.991，CTA分割的最终骰子系数为0.984。此外，attention U-Net 和 nnU-Net 网络也表现良好。在这篇综述论文中，评估了来自不同研究组的 12 个团队选择的方法，并分析了它们的分割结果，以供研究和相关人员参考。