Purpose: To develop an automated method to delineate lens epithelial cells and to quantify expression of fluorescent signal of biomarkers in each nucleus and cytoplasm of lens epithelial cells in a histological section. Methods: An automated algorithm was developed in Matlab™ to localize and quantify fluorescence signal in lens epithelial cells in histological images. A region of interest representing the lens epithelium was manually demarcated in each input image. Individual cell nuclei within the region of interest were automatically delineated based on watershed segmentation and thresholding. Fluorescence signal was quantified within nuclei and cytoplasms. The classification of fluorescence signal was based on local background. Classification of cells as labelled or not labelled was thereafter optimized as compared to visual classification of a limited dataset. The performance of the automated classification was evaluated by asking eleven independent blinded observers to classify all cells (n=395) in one lens image. Time consumed by the automatic algorithm and visual /manual classification of nuclei, was recorded. Results: On an average, 77 % of the cells were correctly classified as compared to the majority vote of the visual observers. The average agreement among visual observers was 83 %. However, variation among visual observers was high, and agreement between two visual observers was as low as 71 % in the worst case. Automated classification was on average 10 times faster than manual scoring. Conclusion: The presented method enables objective and fast detection of lens epithelial cells and quantification of expression of fluorescent signal in a histological section of rat lens, with accuracy comparable to the variability between different visual observers. Furthermore, automated scoring is unbiased and reproducible, and results in a 10-fold increase in throughput.
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