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

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 crosssectional 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.

Development of a quantitative semi-automated system for intestinal morphology assessment in Atlantic salmon, using image analysis

“…Results suggest that the proposed assisted computer system can be a viable alternative to pathologist's scoring in a manner that is more practical and time-efficient. The results obtained agree with previous studies (Galarraga et al, 2012;Pajor et al, 2012;Sieren et al, 2010;Tapias et al, 2013), which also indicated that the automated process greatly reduces the time spent on appraisal, compared with the traditional manual process by effectively transferring the load of the assessment process to the computer.…”

“…As expected, the disparity between the automatic and interactive methods was even smaller (Automated vs. interactive: r Count = 0.924, p Count b 0.001). Accuracy values of the interactive and automated approaches described here are comparable with those of other widely accepted systems (Galarraga et al, 2012;Nielsen et al, 2012;Pajor et al, 2012;Shi et al, 2012;Sieren et al, 2010;Tapias et al, 2013).…”

Development and translational imaging of a TP53 porcine tumorigenesis model

“…Thus, large amounts of data, with tightly controlled cofactors and low population variation, may be achieved, requiring fewer subject numbers for equivalent statistical significance than a human cancer cohort. Furthermore, many cancer types are heterogeneous in content, composed not only of cancerous cells, but also of inflammatory mediators (41), fibrotic stroma (42), necrotic tissue, and complex vascularity (43). Our porcine model does not rely on an immunocompromised host for tumor development, unlike xenograft models (8).…”

An Entropy-Based Automated Cell Nuclei Segmentation and Quantification: Application in Analysis of Wound Healing Process

“…A cell nuclei segmentation algorithm incorporating unsupervised color clustering, morphological operations, and local thresholding has been proposed to distinguish the cancerous and noncancerous areas in histologically stained images and then segment the clustered cell nuclei [3]. K-means clustering is implemented as unsupervised color clustering technique for cell nuclei segmentation in [4]. Another technique that uses contour detection and contour optimization combined with local gradient information and color deconvolution has been used to detect the optimal threshold values for nuclei segmentation [5].…”