An automated segmentation approach for highlighting the histological complexity of human lung cancer.
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.
Sieren
J C
JC
Department of Internal Medicine, University of Iowa, Iowa City, USA. jessica-sieren@uiowa.edu
Weydert
J
J
Bell
A
A
De Young
B
B
Smith
A R
AR
Thiesse
J
J
Namati
E
E
McLennan
Geoffrey
G
D000465:Algorithms; D001698:Biomedical Engineering; D003936:Diagnosis, Computer-Assisted; D006652:Histological Techniques / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007150:Immunohistochemistry / Q000379:methods; D008175:Lung Neoplasms / Q000145:classification / Q000473:pathology*; D013194:Staining and Labeling / Q000379:methods