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Automatic Segmentation of Ground-Glass Opacities in Lung CT Images by Using Markov Random Field-Based Algorithms

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Abstract

Chest radiologists rely on the segmentation and quantificational analysis of ground-glass opacities (GGO) to perform imaging diagnoses that evaluate the disease severity or recovery stages of diffuse parenchymal lung diseases. However, it is computationally difficult to segment and analyze patterns of GGO while compared with other lung diseases, since GGO usually do not have clear boundaries. In this paper, we present a new approach which automatically segments GGO in lung computed tomography (CT) images using algorithms derived from Markov random field theory. Further, we systematically evaluate the performance of the algorithms in segmenting GGO in lung CT images under different situations. CT image studies from 41 patients with diffuse lung diseases were enrolled in this research. The local distributions were modeled with both simple and adaptive (AMAP) models of maximum a posteriori (MAP). For best segmentation, we used the simulated annealing algorithm with a Gibbs sampler to solve the combinatorial optimization problem of MAP estimators, and we applied a knowledge-guided strategy to reduce false positive regions. We achieved AMAP-based GGO segmentation results of 86.94%, 94.33%, and 94.06% in average sensitivity, specificity, and accuracy, respectively, and we evaluated the performance using radiologists’ subjective evaluation and quantificational analysis and diagnosis. We also compared the results of AMAP-based GGO segmentation with those of support vector machine-based methods, and we discuss the reliability and other issues of AMAP-based GGO segmentation. Our research results demonstrate the acceptability and usefulness of AMAP-based GGO segmentation for assisting radiologists in detecting GGO in high-resolution CT diagnostic procedures.

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Acknowledgments

The project was supported by the grants from the Major State Basic Research Development Program of China (973 Program) (no. 2010CB834302) and the National Nature Science Foundation of China (grant no. 30570512), as well as International Collaborative Project of Shanghai-NRC (no. 09540701200).

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Authors and Affiliations

  1. Shanghai Institute of Technical Physics, Chinese Academy of Sciences, 500 Yu Tian Road, Shanghai, 200083, China

    Yanjie Zhu, Yongqing Tan & Jianguo Zhang

  2. Department of Radiology, Huadong Hospital, Shanghai, China

    Yanqing Hua & Guozhen Zhang

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  1. Yanjie Zhu
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  2. Yongqing Tan
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  3. Yanqing Hua
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  4. Guozhen Zhang
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  5. Jianguo Zhang
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Correspondence to Jianguo Zhang.

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Zhu, Y., Tan, Y., Hua, Y. et al. Automatic Segmentation of Ground-Glass Opacities in Lung CT Images by Using Markov Random Field-Based Algorithms. J Digit Imaging 25, 409–422 (2012). https://doi.org/10.1007/s10278-011-9435-5

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