Color Correction of Stained Tissue Section Images by Histogram Transfer According to Control Images.

OBJECTIVE: To present a color correction method by histogram transfer depending upon control tissue image (CTI) differences, and subsequently evaluate its performance. STUDY DESIGN: Images from colon and placenta sections stained by anti-CD34 were used as CTI and/ or sample tissue images (STIs). In total, 36 slides were stained: 1 according to standard procedure and 35 with some variation in the durations or dilutions used for the staining process. For hematoxylin and eosin (20 slides) and Van Gieson (20 slides) stains, colonic mucosa and liver tissues were used. Digital images without normalization were taken by a CCD camera connected to a light microscope and stored on a computer. A software tool was developed in order to find the histogram difference between 2 CTIs and transfer the difference to the STI for achieving a corrected STI (corSTI). sSTI (1 image) and STI and corSTI (for each image) were semiquantitatively scored by 2 observers in blind fashion, and the STI and corSTI scores were compared with the sSTI score. Total optic density (TOD) and median optic density (MOD) and intensity were also calculated by the software. RESULTS: The STI semiquantitative score was equal to the sSTI in 23.5% of the image; this improved to 76.35% when the corSTI was compared to the sSTI. The concordance of TOD and intensity values of CD34-stained placenta images, as well as TOD and MOD values of H&E-stained colonic mucosa images, with the values calculated for the sSTI, increased following image correction. CONCLUSION: These results suggest that histogram transfer depending upon CTIs may be a valuable tool for color correction of tissue section images.

A review on color normalization and color deconvolution methods in histopathology.

The histopathologists get the benefits of wide range of colored dyes to have much useful information about the lesions and the tissue compositions. Despite its advantages, the staining process comes up with quite complex variations in staining concentrations and correlations, tissue fixation types, and fixation time periods. Together with the improvements in computing power and with the development of novel image analysis methods, these imperfections have led to the emerging of several color normalization algorithms. This article is a review of the currently available digital color normalization methods for the bright field histopathology. We describe the proposed color normalization methodologies in detail together with the lesion and tissue types used in the corresponding experiments. We also present the quantitative validation approaches for each of the proposed methodology where available.

Automated labeling of cancer textures in larynx histopathology slides using quasi-supervised learning.

OBJECTIVE: To evaluate the performance of a quasi-supervised statistical learning algorithm, operating on datasets having normal and neoplastic tissues, to identify larynx squamous cell carcinomas. Furthermore, cancer texture separability measures against normal tissues are to be developed and compared either for colorectal or larynx tissues. STUDY DESIGN: Light microscopic digital images from histopathological sections were obtained from laryngectomy materials including squamous cell carcinoma and nonneoplastic regions. The texture features were calculated by using co-occurrence matrices and local histograms. The texture features were input to the quasi-supervised learning algorithm. RESULTS: Larynx regions containing squamous cell carcinomas were accurately identified, having false and true positive rates up to 21% and 87%, respectively. CONCLUSION: Larynx squamous cell carcinoma versus normal tissue texture separability measures were higher than colorectal adenocarcinoma versus normal textures for the colorectal database. Furthermore, the resultant labeling performances for all larynx datasets are higher than or equal to that of colorectal datasets. The results in larynx datasets, in comparison with the former colorectal study, suggested that quasi-supervised texture classification is to be a helpful method in histopathological image classification and analysis.

Automated labelling of cancer textures in colorectal histopathology slides using quasi-supervised learning.

Quasi-supervised learning is a statistical learning algorithm that contrasts two datasets by computing estimate for the posterior probability of each sample in either dataset. This method has not been applied to histopathological images before. The purpose of this study is to evaluate the performance of the method to identify colorectal tissues with or without adenocarcinoma. Light microscopic digital images from histopathological sections were obtained from 30 colorectal radical surgery materials including adenocarcinoma and non-neoplastic regions. The texture features were extracted by using local histograms and co-occurrence matrices. The quasi-supervised learning algorithm operates on two datasets, one containing samples of normal tissues labelled only indirectly, and the other containing an unlabeled collection of samples of both normal and cancer tissues. As such, the algorithm eliminates the need for manually labelled samples of normal and cancer tissues for conventional supervised learning and significantly reduces the expert intervention. Several texture feature vector datasets corresponding to different extraction parameters were tested within the proposed framework. The Independent Component Analysis dimensionality reduction approach was also identified as the one improving the labelling performance evaluated in this series. In this series, the proposed method was applied to the dataset of 22,080 vectors with reduced dimensionality 119 from 132. Regions containing cancer tissue could be identified accurately having false and true positive rates up to 19% and 88% respectively without using manually labelled ground-truth datasets in a quasi-supervised strategy. The resulting labelling performances were compared to that of a conventional powerful supervised classifier using manually labelled ground-truth data. The supervised classifier results were calculated as 3.5% and 95% for the same case. The results in this series in comparison with the benchmark classifier, suggest that quasi-supervised image texture labelling may be a useful method in the analysis and classification of pathological slides but further study is required to improve the results.

The prognostic value of tumor-stroma proportion in laryngeal squamous cell carcinoma.

OBJECTIVE: Tumor-stroma proportion of tumor has been presented as a prognostic factor in some types of adenocarcinomas, but there is no information about squamous cell carcinomas and laryngeal carcinomas. MATERIAL AND METHOD: Five digital images of the tumor sections were obtained from 85 laryngeal carcinomas. Proportion of epithelial tumor component and stroma were measured by a software tool, allowing the pathologists to mark 205.6 µm2 blocks on areas as carcinomatous/stromal, by clicking at the image. Totally, 3.451 mm2 tumor areas have been marked to 16.785 small square blocks for each case. RESULTS: Median follow up was 48 months (range 3-194). The mean tumor-stroma proportion was 48.63+18.18. There was no difference for tumor-stroma proportion when tumor location, grade, stage and perinodal invasion were considered. Although the following results were statistically insignificant, the mean tumor-stroma proportion was the lowest (37.46±12.49) for subglottic carcinomas, and it was 52.41±37.47, 50.86+19.84 and 44.56±16.91 for supraglottic, transglottic and glottic cases. The tumor-stroma proportion was lowest in cases with perinodal invasion and the highest in cases without lymph node metastasis (44.72±20.23, 47.77±17.37, 50.05±17.34). Tumor-stroma proportion was higher in the basaloid subtype compared with the classical squamous cell carcinoma (53.76±14.70 and 48.63±18.38 respectively). The overall and disease-free survival analysis did not reveal significance for tumor-stroma proportion (p=0.08, p=0.38). Only pathological stage was an independent factor for overall survival (p=0.008). CONCLUSION: This is the first series investigating tumor-stroma proportion as a prognostic marker in laryngeal carcinomas proposing a new method, but the findings do not support tumor-stroma proportion as a prognostic marker.