Application of artificial intelligence cloud technology in auto-segmentation of cardiac substructure of breast cancer patients(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2020年第12期
- Page:
- 1599-1603
- Research Field:
- 医学人工智能
- Publishing date:
Info
- Title:
- Application of artificial intelligence cloud technology in auto-segmentation of cardiac substructure of breast cancer patients
- Author(s):
- CHEN Ziyin1; BAI Yanchun1; XU Wei2; WANG Dingyu2; XU Lili1; ZHAO Qiushuang1; ZHU Senhua3; WANG Yang4; LIU Guangzhong2
- 1. Department of Oncology Radiotherapy, the First Affiliated Hospital of Harbin Medical University, Harbin 150001, China 2. The Fifth Cardiology Department, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China 3 Lingkingmed Science and Technology Company, Beijing 100083, China 4. Accelerator Room, Harbin Chest Hospital, Harbin 150056, China
- Keywords:
- Keywords: artificial intelligence cloud technology auto-segmentation breast cancer heart substructure
- PACS:
- R318；R811.1
- DOI:
- DOI:10.3969/j.issn.1005-202X.2020.12.024
- Abstract:
- Abstract: Objective To evaluate the accuracy and feasibility of artificial intelligence cloud technology contouring platform (AI Contour) in auto-segmentation of cardiac substructure of breast cancer patients. Methods The vascular enhanced CTs from 10 patients underwent breast cancer radiotherapy from July 2019 to December 2019 were selected as the research object. On AI Contour, manual segmentation, auto-segmentation and manual modification after auto-segmentation were used to segment the cardiac substructures of 10 patients, including left atrium, right atrium, left ventricle and right ventricle. The differences of data in Dice similarity coefficient (DSC), Jaccard coefficient (JC), Hausdorf distance (HD), Center of Mass Deviation (CMD), inclusiveness coefficient (IncI), sensitivity index (SI), and segmentation time were compared. Results With manual segmentation as the gold standard, the results of each cardiac substructure made by auto-segmentation and manual segmentation have differences of DSC>0.8, JC>0.6, HD<9 mm, CMD<5 mm, IncI>0.8, SI>0.7. Manual modification after auto-segmentation further improved the segmentation accuracy, in which JC>0.8. The time of auto-segmentation and manual segmentation were (85.50±6.06) s vs. (1160.30±74.31) s respectively, and the difference was statistically significant (P<0.05). The total time of manual modification after auto-segmentation and manual segmentation were (558.70±33.40) s vs. (1160.30±74.31) s, and the difference was statistically significant (P<0.05). Conclusions Through comparison, it was found that the auto-segmentation technique can segment the left atrium, right atrium, left ventricle, and right ventricle of breast cancer patients with high accuracy, saving a lot of time. Manual modification after auto-segmentation can further improve the segmentation accuracy of cardiac substructure of each part. The cloud segmentation platform has the advantage of remote collaboration, and is worth popularizing.
Last Update: 2020-12-30