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:
[*]
Corresponding author: Wenjun Tan and Jinzhu Yang, Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, College of Computer Science and Engineering, Northeastern University, Shenyang 110189, China. E-mails: tanwenjun@cse.neu.edu.cn (Wenjun Tan), yangjinzhu@cse.neu.edu.cn (Jinzhu Yang) and Yufei Chen, College of Electronics and Information Engineering, Tongji University, Shanghai 200092, China. E-mail: yufeichen@tongji.edu.cn.
Abstract: 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 improve the 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 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.
Keywords: Deep learning, computed tomography (CT), computed tomography angiography (CTA), segmentation of lung parenchyma, U-Net, nnU-Net
