24 March 2008 Comprehensive evaluation of an image segmentation technique for measuring tumor volume from CT images
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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.
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Xiang Deng, Xiang Deng, Haibin Huang, Haibin Huang, Lei Zhu, Lei Zhu, Guangwei Du, Guangwei Du, Xiaodong Xu, Xiaodong Xu, Yiyong Sun, Yiyong Sun, Chenyang Xu, Chenyang Xu, Marie-Pierre Jolly, Marie-Pierre Jolly, Jiuhong Chen, Jiuhong Chen, Jie Xiao, Jie Xiao, Reto Merges, Reto Merges, Michael Suehling, Michael Suehling, Daniel Rinck, Daniel Rinck, Lan Song, Lan Song, Zhengyu Jin, Zhengyu Jin, Zhaoxia Jiang, Zhaoxia Jiang, Bin Wu, Bin Wu, Xiaohong Wang, Xiaohong Wang, Shuai Zhang, Shuai Zhang, Weijun Peng, Weijun Peng, } "Comprehensive evaluation of an image segmentation technique for measuring tumor volume from CT images", Proc. SPIE 6917, Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment, 691705 (24 March 2008); doi: 10.1117/12.769619; https://doi.org/10.1117/12.769619
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