Comparative study of deep learning- versus Atlas-based auto-segmentation of organs-at-risk in tumor radiotherapy(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2019年第12期
- Page:
- 1486-1490
- Research Field:
- 其他（激光医学等）
- Publishing date:
Info
- Title:
- Comparative study of deep learning- versus Atlas-based auto-segmentation of organs-at-risk in tumor radiotherapy
- Author(s):
- ZHANG Fuli1; CUI Deqi2; WANG Qiusheng3; WEI Lingyu1; ZHU Linlin2; YU Yanjun1; LI Haipeng3; WANG Yadi1
- 1. the Seventh Medical Center of Chinese PLA General Hospital, Beijing 100700, China; 2. Beijing Linkingmed Science and Technology Company, Beijing 100083, China; 3. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, China
- Keywords:
- Keywords: deep learning; Atlas; organs-at-risk; auto-segmentation; tumor radiotherapy
- PACS:
- R318；R811.1
- DOI:
- DOI:10.3969/j.issn.1005-202X.2019.12.024
- Abstract:
- Abstract: Objective To evaluate and compare the geometric accuracy between deep learning (DL)- and Altas-based auto-segmentation technologies for contouring organs-at-risk (OARs) in radiotherapy for tumors locating in different sites so as to provide a basis for the clinical application. Methods The OARs in CT images of 40 patients with tumors in different sites (head and neck, thorax, abdomen, and pelvic cavity) were manually segmented by senior physicians, and then automatically segmented by DL- and Atlas-based auto-segmentation methods. Several evaluation indicators such as Dice coefficient (DC), Jaccard coefficient (JC), Hausdorff distance (HD) and volume difference (VD) were used to evaluate the geometric accuracy between DL- or Atlas-based auto-segmentations and manual segmentation. Results The DC values of OARs except for rectum segmented by DL-based method were higher than 0.7, higher than the results obtained by Atlas-based method, with statistical significance (P<0.05). In addition, the JC values obtained by DL-based method were also higher than 0.7, except for the JC values of lens, rectum and spinal cord. Spinal cord had the highest HD value, exceeding 20 mm in both methods. The rectum segmented by DL method had relatively high absolute VD. Conclusion The geometric accuracy of DL-based auto-segmentation is generally superior to that of Atlas-based auto-segmentation. In the further study, the robustness of DL model will be increased by expanding the training dataset, thereby better assisting radiation oncologists in routine clinical work and bringing benefits to tumor patients.
Last Update: 2019-12-26