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An improved supervoxel 3D region growing method based on PET/CT multimodal data for segmentation and reconstruction of GGNs

  • Yunyun Dong
  • Wenkai Yang
  • Jiawen Wang
  • Zijuan Zhao
  • Sanhu Wang
  • Qiang Cui
  • Yan QiangEmail author
Article
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Abstract

Among the various types of lung nodules, ground glass nodules (GGNs) are difficult to segment accurately due to complex morphological characteristics. Moreover, GGNs are associated with a higher malignancy probability. Three-dimensional (3D) segmentation and reconstruction techniques can help physicians intuitively elucidate the relationship between lung nodules and their surrounding tissues. We propose an improved supervoxel 3D region growing approach based on positron emission tomography/computed tomography (PET/CT) multimodal data for the segmentation and reconstruction of GGNs. First, the seed point is automatically located with PET information and a 3D mask is generated. Then, a fuzzy connectivity (FC) map is generated based on the 3D mask, and an improved supervoxel 3D region growing is utilized on a fuzzy connectivity map under the constraints of the 3D mask. Finally, 3D GGNs segmentation and reconstruction results are obtained. Qualitative and quantitative comparisons between our proposed method and other region growing methods shows great superiority of our proposed method, with the Jaccard similarity coefficient between our proposed method and physician manual segmentation reaching 95.61%; the average processing time is 16.38 s. Experimental results show that our proposed supervoxel-based 3D region growing method is very promising for assisting physicians in diagnosis.

Keywords

Multimodal data Supervoxel Fuzzy connectivity map Region growing 

Notes

Acknowledgements

This research was funded in part by National Natural Science Foundation of China, grant number 61872261, in part by the open funding project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (Grant No. VRLAB2018B07) and in part by Research Project Supported by Shanxi Natural Science Foundation (201801D121139).

Author Contributions

writing—review and editing Q.Y and D.Y.; methodology Y.W. and C.Q; software W.J., validation Z.Z.; formal analysis, W.S.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Yunyun Dong
    • 1
  • Wenkai Yang
    • 1
  • Jiawen Wang
    • 1
  • Zijuan Zhao
    • 1
  • Sanhu Wang
    • 2
  • Qiang Cui
    • 1
  • Yan Qiang
    • 1
    Email author
  1. 1.College of Information and ComputerTaiyuan University of TechnologyTaiyuanChina
  2. 2.Department of Computer Science and TechnologyLvliang UniversityLvliangChina

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