Quantitative evaluation of Atlas-based auto-segmentation of organs-at-risk in patients with cervical
cancer using different Atlas database sizes(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2019年第7期
- Page:
- 760-764
- Research Field:
- 医学放射物理
- Publishing date:
Info
- Title:
- Quantitative evaluation of Atlas-based auto-segmentation of organs-at-risk in patients with cervical
cancer using different Atlas database sizes
- Author(s):
- WANG Jinyuan1; XU Shouping1; 2; LIU Bo3; ZHENG Qingzeng4; ZHANG Huijuan1; YANGWei1; QU Baolin1; 2
- 1. Department of Radiotherapy, Chinese PLA General Hospital, Beijing 100853, China; 2. Beihang Advanced Innovation Center for Big
Date- based Precision Medicine, Beijing 100083, China; 3. Image Processing Center, Beihang University, Beijing 100191, China; 4.
Department of Radiotherapy, Beijing Geriatric Hospital, Beijing 100095, China
- Keywords:
- Keywords: Atlas; cervical cancer; Atlas-based auto-segmentation; organs-at-risk; quantitative analysis
- PACS:
- R730.55；R318
- DOI:
- DOI:10.3969/j.issn.1005-202X.2019.07.004
- Abstract:
- Abstract: Objective To evaluate the effects of Atlas-based auto-segmentation (ABAS) with increasing Atlas database sizes on
the automatic segmentation of organs-at-risk in patients with cervical cancer for obtaining the optimal Atlas size. Methods Four
sets of cervical cancer Atlas databases, with 30, 60, 90 and 120 cases, respectively, were established with MIM software. The
images of another 10 patients with cervical cancer were selected out of Atlas databases, and the organs-at-risk including bladder,
rectum and bilateral femoral heads were segmented by an experienced radiation oncologist, and the obtained results were defined
as reference volumes. Meanwhile, the 4 organs-at-risk of 10 patients were automatically segmented withABAS, with the matching
number of 3 and 9, respectively. The time for segmentation, Dice similarity coefficient, sensitivity index, inclusiveness index,
deviation of centroid, Jaccard coefficient and Hausdorff distance were quantitatively evaluated, and a one-way analysis of variance
was performed on the results obtained with ABAS and reference contours, thereby discussing the effect of Altas database size
on automatic segmentation. Results For the matching number of 3 and 9, the mean time of 4 sets of Atlas databases for automatic
segmentation was shorter than that of manual segmentation (1.31/1.33/1.35/1.39 min vs 10.25 min; 5.07/5.24/5.14/5.24 min vs
10.25 min), but there was no significant difference among different groups. When the matching number was 3, statistical differences were found in the sensitivity index of bladder and rectum (P=0.018, 0.010), the Dice similarity coefficient, Jaccard
coefficient and Hausdorff distance of rectum (P=0.016, 0.013, 0.042). When the matching number was 9, statistical difference
was only found in the Hausdorff distance of rectum (P=0.002). The differences in the other parameters were trivial. Conclusion
ABAS can shorten the time for segmentation, and the increasing of Atlas database size doesn’t affect the segmentation efficiency.
The Atlas database with 30 cases has a poor performance in segmentation, while the Atlas database with 60 cases is advantageous
in the accuracy of the segmentation of bladder and rectum. Considering the time cost,Atlas database with 60 cases is recommended
for the clinical application.
Last Update: 2019-07-24