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24 March 2008 Comprehensive evaluation of an image segmentation technique for measuring tumor volume from CT images
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
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Proceedings Volume 6917, Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment; 691705 (2008) https://doi.org/10.1117/12.769619
Event: Medical Imaging, 2008, San Diego, California, United States
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, 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, and 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); https://doi.org/10.1117/12.769619
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KEYWORDS
Image segmentation
Tumors
Lung
Image processing algorithms and systems
Computed tomography
Liver
Lymphoma
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