Affiliations: [a] Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, China
| [b]
College of Computer Science and Engineering, Northeastern University, Shenyang, China
| [c]
College of Electronics and Information Engineering, Tongji University, Shanghai, China
Correspondence:
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Corresponding authors. Wenjun Tan and Jinzhu Yang, Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang 110189, China. E-mails: [email protected] (Wenjun Tan), [email protected] (Jinzhu Yang) and Yufei Chen, College of Electronics and Information Engineering, Tongji University, Shanghai 200092, China. E-mail: [email protected].
Abstract: 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 rate 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.
