Abstract
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.
Highlights
The structural and fluorescent properties of the retinal pigmented epithelium (RPE) cells in the retina are affected by several eye diseases such as age-related macular dystrophy (AMD) [1], glaucoma [2], and cone-rod dystrophy [3]
Two-photon fluorescence microscopy (TPM) allows non-invasive imaging of the RPE when paired with adaptive optics [4]
Segmenting retinal images from fluorescent scanning laser ophthalmoscope (SLO) resulted in missing an average of 19 / 206 cells or a 91% success rate; on simulated noisy data, this method achieved a success rate of 97.21% [9]
Summary
The structural and fluorescent properties of the retinal pigmented epithelium (RPE) cells in the retina are affected by several eye diseases such as age-related macular dystrophy (AMD) [1], glaucoma [2], and cone-rod dystrophy [3]. Two-photon fluorescence microscopy (TPM) allows non-invasive imaging of the RPE when paired with adaptive optics [4]. TPM can resolve and differentiate between retinosomes and damaging products of the visual cycle sequestered in the RPE [5]. Retinosomes contain retinyl esters, components of the retinoid cycle that restore visual pigments to their ‘ready-to-be-activated’ state. Both a lack of retinosomes and their increased dimensions could be indicative of a malfunctioning retinoid cycle as demonstrated by Lrat–/– and Rpe65–/– mice, respectively. To take advantage of the powerful capabilities of TPM, as with all imaging modalities, automated segmentation methods will be critical for analysis of imaging data sets [6]
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