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Automated image segmentation is a critical step toward achieving a quantitative evaluation of disease states with imaging techniques. Two-photon fluorescence microscopy (TPM) has been employed to visualize the retinal pigmented epithelium (RPE) and provide images indicating the health of the retina. However, segmentation of RPE cells within TPM images is difficult due to small differences in fluorescence intensity between cell borders and cell bodies. Here we present a semi-automated method for segmenting RPE cells that relies upon multiple weak features that differentiate cell borders from the remaining image. These features were scored by a search optimization procedure that built up the cell border in segments around a nucleus of interest. With six images used as a test, our method correctly identified cell borders for 69% of nuclei on average. Performance was strongly dependent upon increasing retinosome content in the RPE. TPM image analysis has the potential of providing improved early quantitative assessments of diseases affecting the RPE.

Semi-automated discrimination of retinal pigmented epithelial cells in two-photon fluorescence images of mouse retinas