BACKGROUND:The distribution of pulmonary vessels in computed tomography (CT) and computed tomography angiography (CTA) images of lung is important for diagnosing disease, formulating surgical plans and pulmonary research. PURPOSE:Based on the pulmonary vascular segmentation task of International Symposium on Image Computing and Digital Medicine 2020 challenge, this paper reviews 12 different pulmonary vascular segmentation algorithms of lung CT and CTA images and then objectively evaluates and compares their performances. METHODS:First, we present the annotated reference dataset of lung CT and CTA images. A subset of the dataset consisting 7,307 slices for training and 3,888 slices for testing was made available for participants. Second, by analyzing the performance comparison of different convolutional neural networks from 12 different institutions for pulmonary vascular segmentation, the reasons for some defects and improvements are summarized. The models are mainly based on U-Net, Attention, GAN, and multi-scale fusion network. The performance is measured in terms of Dice coefficient, over segmentation ratio and under segmentation rate. Finally, we discuss several proposed methods to improve the pulmonary vessel segmentation results using deep neural networks. RESULTS:By comparing with the annotated ground truth from both lung CT and CTA images, most of 12 deep neural network algorithms do an admirable job in pulmonary vascular extraction and segmentation with the dice coefficients ranging from 0.70 to 0.85. The dice coefficients for the top three algorithms are about 0.80. CONCLUSIONS:Study results show that integrating methods that consider spatial information, fuse multi-scale feature map, or have an excellent post-processing to deep neural network training and optimization process are significant for further improving the accuracy of pulmonary vascular segmentation.

中文翻译：

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通过深度神经网络对肺部 CT 和 CTA 图像进行自动血管分割

背景：肺部计算机断层扫描（CT）和计算机断层扫描血管造影（CTA）图像中肺血管的分布对于诊断疾病、制定手术方案和肺部研究具有重要意义。目的：基于2020年国际图像计算和数字医学挑战赛的肺血管分割任务，本文回顾了12种不同的肺CT和CTA图像肺血管分割算法，然后对其性能进行客观评估和比较。方法：首先，我们展示了肺部 CT 和 CTA 图像的注释参考数据集。为参与者提供了包含 7,307 个训练切片和 3,888 个测试切片的数据集子集。第二，通过分析来自12个不同机构的不同卷积神经网络在肺血管分割方面的性能比较，总结了一些缺陷和改进的原因。模型主要基于 U-Net、Attention、GAN 和多尺度融合网络。性能是根据 Dice 系数、过分割率和欠分割率来衡量的。最后，我们讨论了几种使用深度神经网络改善肺血管分割结果的方法。结果：通过与肺部 CT 和 CTA 图像标注的 ground truth 进行比较，12 种深度神经网络算法中的大多数在肺血管提取和分割方面都表现出色，骰子系数在 0.70 到 0.85 之间。前三种算法的骰子系数约为 0.80。