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

J. C Sieren; J Weydert; A Bell; B De Young; A. R Smith; J Thiesse; E Namati; Geoffrey McLennan

doi:10.1007/s10439-010-0103-6

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

Journal article

Peer reviewed

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

J. C Sieren, J Weydert, A Bell, B De Young, A. R Smith, J Thiesse, E Namati and Geoffrey McLennan

Annals of biomedical engineering, Vol.38(12), pp.3581-3591

12/2010

DOI: 10.1007/s10439-010-0103-6

PMCID: PMC2996273

PMID: 20571856

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https://www.ncbi.nlm.nih.gov/pmc/articles/2996273View

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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.

Histology

Immunohistochemistry

Pathology

Adenocarcinoma

Quantification

Lung nodule

Computer analysis

Details

Title: Subtitle: An Automated Segmentation Approach for Highlighting the Histological Complexity of Human Lung Cancer
Creators: J. C Sieren - Department of Internal Medicine, University of Iowa, Iowa City, IA, USA
J Weydert - Department of Surgical Pathology, University of Iowa, Iowa City, IA, USA
A Bell - Department of Surgical Pathology, University of Iowa, Iowa City, IA, USA
B De Young - Department of Surgical Pathology, University of Iowa, Iowa City, IA, USA
A. R Smith - Department of Internal Medicine, University of Iowa, Iowa City, IA, USA
J Thiesse - Department of Internal Medicine, University of Iowa, Iowa City, IA, USA
E Namati - Department of Internal Medicine, University of Iowa, Iowa City, IA, USA
Geoffrey McLennan - Department of Internal Medicine, University of Iowa, Iowa City, IA, USA
Resource Type: Journal article
Publication Details: Annals of biomedical engineering, Vol.38(12), pp.3581-3591
DOI: 10.1007/s10439-010-0103-6
PMID: 20571856
PMCID: PMC2996273
NLM abbreviation: Ann Biomed Eng
ISSN: 0090-6964
eISSN: 1573-9686
Language: English
Date published: 12/2010
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology
Record Identifier: 9984051792102771

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