Share Email Print
cover

Proceedings Paper

Comprehensive evaluation of an image segmentation technique for measuring tumor volume from CT images
Author(s): Xiang Deng; Haibin Huang; Lei Zhu; Guangwei Du; Xiaodong Xu; Yiyong Sun; Chenyang Xu; Marie-Pierre Jolly; Jiuhong Chen; Jie Xiao; Reto Merges; Michael Suehling; Daniel Rinck; Lan Song; Zhengyu Jin; Zhaoxia Jiang; Bin Wu; Xiaohong Wang; Shuai Zhang; Weijun Peng
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Comprehensive quantitative evaluation of tumor segmentation technique on large scale clinical data sets is crucial for routine clinical use of CT based tumor volumetry for cancer diagnosis and treatment response evaluation. In this paper, we present a systematic validation study of a semi-automatic image segmentation technique for measuring tumor volume from CT images. The segmentation algorithm was tested using clinical data of 200 tumors in 107 patients with liver, lung, lymphoma and other types of cancer. The performance was evaluated using both accuracy and reproducibility. The accuracy was assessed using 7 commonly used metrics that can provide complementary information regarding the quality of the segmentation results. The reproducibility was measured by the variation of the volume measurements from 10 independent segmentations. The effect of disease type, lesion size and slice thickness of image data on the accuracy measures were also analyzed. Our results demonstrate that the tumor segmentation algorithm showed good correlation with ground truth for all four lesion types (r = 0.97, 0.99, 0.97, 0.98, p < 0.0001 for liver, lung, lymphoma and other respectively). The segmentation algorithm can produce relatively reproducible volume measurements on all lesion types (coefficient of variation in the range of 10-20%). Our results show that the algorithm is insensitive to lesion size (coefficient of determination close to 0) and slice thickness of image data(p > 0.90). The validation framework used in this study has the potential to facilitate the development of new tumor segmentation algorithms and assist large scale evaluation of segmentation techniques for other clinical applications.

Paper Details

Date Published: 24 March 2008
PDF: 8 pages
Proc. SPIE 6917, Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment, 691705 (24 March 2008); doi: 10.1117/12.769619
Show Author Affiliations
Xiang Deng, Siemens Ltd. China (China)
Haibin Huang, Siemens Ltd. China (China)
Lei Zhu, Siemens Ltd. China (China)
Guangwei Du, Siemens Ltd. China (China)
Xiaodong Xu, Siemens Ltd. China (China)
Yiyong Sun, Siemens Corporate Research (United States)
Chenyang Xu, Siemens Corporate Research (United States)
Marie-Pierre Jolly, Siemens Corporate Research (United States)
Jiuhong Chen, Siemens Ltd. China (China)
Jie Xiao, Siemens Ltd. China (China)
Reto Merges, Siemens Ltd. China (China)
Michael Suehling, Siemens Medical Solutions (Germany)
Daniel Rinck, Siemens Medical Solutions (Germany)
Lan Song, Peking Union Medical College Hospital (China)
Zhengyu Jin, Peking Union Medical College Hospital (China)
Zhaoxia Jiang, FuDan Univ. Cancer Hospital (China)
Bin Wu, FuDan Univ. Cancer Hospital (China)
Xiaohong Wang, FuDan Univ. Cancer Hospital (China)
Shuai Zhang, FuDan Univ. Cancer Hospital (China)
Weijun Peng, FuDan Univ. Cancer Hospital (China)


Published in SPIE Proceedings Vol. 6917:
Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment
Berkman Sahiner; David J. Manning, Editor(s)

© SPIE. Terms of Use
Back to Top