Lung nodule growth analysis from 3D CT data with a coupled segmentation and registration framework

Yuanjie Zheng, Karl Steiner, Thomas Bauer, Jingyi Yu, Dinggang Shen, Chandra Kambhamettu

Research output: Contribution to conferencePaper

29 Citations (Scopus)

Abstract

In this paper we propose a new framework to simultaneously segment and register lung and tumor in serial CT data. Our method assumes nonrigid transformation on lung deformation and rigid structure on the tumor. We use the B-Spline-based nonrigid transformation to model the lung deformation while imposing rigid transformation on the tumor to preserve the volume and the shape of the tumor. In particular, we set the control points within the tumor to form a control mesh and thus assume the tumor region follows the same rigid transformation as the control mesh. For segmentation, we apply a 2D graph-cut algorithm on the 3D lung and tumor datasets. By iteratively performing segmentation and registration, our method achieves highly accurate segmentation and registration on serial CT data. Finally, since our method eliminates the possible volume variations of the tumor during registration, we can further estimate accurately the tumor growth, an important evidence in lung cancer diagnosis. Initial experiments on five sets of patients' serial CT data show that our method is robust and reliable.

Original languageEnglish
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 IEEE 11th International Conference on Computer Vision, ICCV - Rio de Janeiro, Brazil
Duration: 2007 Oct 142007 Oct 21

Other

Other2007 IEEE 11th International Conference on Computer Vision, ICCV
CountryBrazil
CityRio de Janeiro
Period07/10/1407/10/21

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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    Zheng, Y., Steiner, K., Bauer, T., Yu, J., Shen, D., & Kambhamettu, C. (2007). Lung nodule growth analysis from 3D CT data with a coupled segmentation and registration framework. Paper presented at 2007 IEEE 11th International Conference on Computer Vision, ICCV, Rio de Janeiro, Brazil. https://doi.org/10.1109/ICCV.2007.4409150