3D segmentation of lung CT data with graph-cuts: analysis of parameter sensitivities

Jung won Cha; Neal Dunlap; Brian Wang; Amir Amini

doi:10.1117/12.2219897

29 March 2016 3D segmentation of lung CT data with graph-cuts: analysis of parameter sensitivities

Jung won Cha, Neal Dunlap, Brian Wang, Amir Amini

Author Affiliations +

Proceedings Volume 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging; 97882O (2016) https://doi.org/10.1117/12.2219897
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Lung boundary image segmentation is important for many tasks including for example in development of radiation treatment plans for subjects with thoracic malignancies. In this paper, we describe a method and parameter settings for accurate 3D lung boundary segmentation based on graph-cuts from X-ray CT data¹. Even though previously several researchers have used graph-cuts for image segmentation, to date, no systematic studies have been performed regarding the range of parameter that give accurate results. The energy function in the graph-cuts algorithm requires 3 suitable parameter settings: K, a large constant for assigning seed points, c, the similarity coefficient for n-links, and λ, the terminal coefficient for t-links. We analyzed the parameter sensitivity with four lung data sets from subjects with lung cancer using error metrics. Large values of K created artifacts on segmented images, and relatively much larger value of c than the value of λ influenced the balance between the boundary term and the data term in the energy function, leading to unacceptable segmentation results. For a range of parameter settings, we performed 3D image segmentation, and in each case compared the results with the expert-delineated lung boundaries. We used simple 6-neighborhood systems for n-link in 3D. The 3D image segmentation took 10 minutes for a 512x512x118 ~ 512x512x190 lung CT image volume. Our results indicate that the graph-cuts algorithm was more sensitive to the K and λ parameter settings than to the C parameter and furthermore that amongst the range of parameters tested, K=5 and λ=0.5 yielded good results.

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Jung won Cha, Neal Dunlap, Brian Wang, and Amir Amini "3D segmentation of lung CT data with graph-cuts: analysis of parameter sensitivities", Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 97882O (29 March 2016); https://doi.org/10.1117/12.2219897

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