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An Automated Segmentation Approach for Highlighting the Histological Complexity of Human Lung Cancer

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Abstract

Lung cancer nodules, particularly adenocarcinoma, contain a complex intermixing of cellular tissue types: incorporating cancer cells, fibroblastic stromal tissue, and inactive fibrosis. Quantitative proportions and distributions of the various tissue types may be insightful for understanding lung cancer growth, classification, and prognostic factors. However, current methods of histological assessment are qualitative and provide limited opportunity to systematically evaluate the relevance of lung nodule cellular heterogeneity. In this study we present both a manual and an automatic method for segmentation of tissue types in histological sections of resected human lung cancer nodules. A specialized staining approach incorporating immunohistochemistry with a modified Masson’s Trichrome counterstain was employed to maximize color contrast in the tissue samples for automated segmentation. The developed, clustering-based, fully automated segmentation approach segments complete lung nodule cross-sectional histology slides in less than 1 min, compared to manual segmentation which requires multiple hours to complete. We found the accuracy of the automated approach to be comparable to that of the manual segmentation with the added advantages of improved time efficiency, removal of susceptibility to human error, and 100% repeatability.

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Acknowledgments

The authors thank Dr. M. Iannettoni for support of this research and assistance with patient identification. We also thank Dr. L. Van Natta, Dr. W. Lynch, Dr. K. Parekh, Ms. J. Rick-McGillin, and Ms. K. McLauglin for assisting patient recruitment; Ms. J. Rodgers, Ms. K. Walters, and Mr. A. Stessman for technical assistance with histopathological preparation. Research for this project was supported by funding from the National Institutes of Health (R01 CA129022). The authors have no conflict of interest.

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Authors and Affiliations

  1. Department of Internal Medicine, University of Iowa, Iowa City, IA, USA

    J. C. Sieren, A. R. Smith, J. Thiesse, E. Namati & Geoffrey McLennan

  2. Department of Surgical Pathology, University of Iowa, Iowa City, IA, USA

    J. Weydert, A. Bell & B. De Young

  3. Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA

    J. C. Sieren & Geoffrey McLennan

  4. Department of Radiology, University of Iowa, Iowa City, IA, USA

    Geoffrey McLennan

  5. The Iowa Institute for Biomedical Imaging, University of Iowa, Iowa City, IA, 52242, USA

    J. C. Sieren, A. R. Smith & Geoffrey McLennan

Authors
  1. J. C. Sieren
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  2. J. Weydert
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  3. A. Bell
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  4. B. De Young
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  5. A. R. Smith
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  6. J. Thiesse
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  7. E. Namati
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  8. Geoffrey McLennan
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Correspondence to Geoffrey McLennan.

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Associate Editor Anne Clough oversaw the review of this article.

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Sieren, J.C., Weydert, J., Bell, A. et al. An Automated Segmentation Approach for Highlighting the Histological Complexity of Human Lung Cancer. Ann Biomed Eng 38, 3581–3591 (2010). https://doi.org/10.1007/s10439-010-0103-6

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  • DOI: https://doi.org/10.1007/s10439-010-0103-6

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