title: Automated segmentation of retinal pigment epithelium cells in fluorescence adaptive optics images.
abstract: Adaptive optics (AO) imaging methods allow the histological characteristics of retinal cell mosaics, such as photoreceptors and retinal pigment epithelium (RPE) cells, to be studied in vivo. The high-resolution images obtained with ophthalmic AO imaging devices are rich with information that is difficult and/or tedious to quantify using manual methods. Thus, robust, automated analysis tools that can provide reproducible quantitative information about the cellular mosaics under examination are required. Automated algorithms have been developed to detect the position of individual photoreceptor cells; however, most of these methods are not well suited for characterizing the RPE mosaic. We have developed an algorithm for RPE cell segmentation and show its performance here on simulated and real fluorescence AO images of the RPE mosaic. Algorithm performance was compared to manual cell identification and yielded better than 91% correspondence. This method can be used to segment RPE cells for morphometric analysis of the RPE mosaic and speed the analysis of both healthy and diseased RPE mosaics.
authors: Piero
authors: P
authors: Rangel-Fonseca
authors: Armando
authors: A
authors: Gómez-Vieyra
authors: Daniel
authors: D
authors: Malacara-Hernández
authors: Mario C
authors: MC
authors: Wilson
authors: David R
authors: DR
authors: Williams
authors: Ethan A
authors: EA
authors: Rossi
mesh_terms: D000465:Algorithms; D000818:Animals; D001331:Automation; D003198:Computer Simulation; D004847:Epithelial Cells / Q000502:physiology*; D005453:Fluorescence; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008027:Light; D008251:Macaca; D008856:Microscopy, Fluorescence / Q000379:methods; D016011:Normal Distribution; D009887:Ophthalmoscopy / Q000379:methods; D055095:Optics and Photonics; D055213:Retinal Pigment Epithelium / Q000166:cytology / Q000473:pathology*