Correlation between a deep-learning-based model observer and human observer for a realistic lung nodule localization task in chest CT

Hao Gong, Andrew Walther, Qiyuan Hu, Chi Wan Koo, Edwin A. Takahashi, David L Levin, Tucker F. Johnson, Megan J. Hora, Shuai Leng, J. G. Fletcher, Cynthia H. McCollough, Lifeng Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Mathematical model observers (MOs) have become popular in task-based CT image quality assessment, since, once proven to be correlated with human observers (HOs), these MOs can be used to estimate HO performance. However, typical MO studies are limited to phantom data which only involve uniform background. In practice, anatomical background variability and tissue non-uniformity affect HO lesion detection performance. Recently, we have proposed a deep-learning-based MO (DL-MO). In this study, we aim to investigate the correlation between this DL-MO and HOs for a lung-nodule localization task in chest CT. Using a patient database that contains 50 lung cancer screening CT patient cases, 12 different experimental conditions were generated, including 4 radiation dose levels, 3 nodule sizes, 2 nodule types and 3 reconstruction types. These conditions were created by using a validated noise and lesion insertion tool. Four subspecialized radiologists performed the HO study for all 12 conditions individually in a randomized fashion. The DL-MO was trained and tested for the same dataset. The performance of DL-MO and HO was compared across all the experimental conditions. DL-MO performance was strongly correlated with HO performance (Pearson's correlation coefficient: 0.988 with a 95% confidence interval of [0.894, 0.999]). These results demonstrate the potential to use the proposed DL-MO to predict HO performance for the task of lung nodule localization in chest CT.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2019
Subtitle of host publicationImage Perception, Observer Performance, and Technology Assessment
EditorsRobert M. Nishikawa, Frank W. Samuelson
PublisherSPIE
ISBN (Electronic)9781510625518
DOIs
StatePublished - Jan 1 2019
EventMedical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment - San Diego, United States
Duration: Feb 20 2019Feb 21 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10952
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment
CountryUnited States
CitySan Diego
Period2/20/192/21/19

Keywords

  • Deep learning
  • Lung nodule detection
  • Model observer
  • Partial least square regression
  • Taskbased image quality assessment
  • X-ray CT

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Gong, H., Walther, A., Hu, Q., Koo, C. W., Takahashi, E. A., Levin, D. L., Johnson, T. F., Hora, M. J., Leng, S., Fletcher, J. G., McCollough, C. H., & Yu, L. (2019). Correlation between a deep-learning-based model observer and human observer for a realistic lung nodule localization task in chest CT. In R. M. Nishikawa, & F. W. Samuelson (Eds.), Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment [109520K] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10952). SPIE. https://doi.org/10.1117/12.2513451