We develop an improved region growing method to realize automatic retinal pigment epithelium (RPE) cell segmentation for photoacoustic microscopy (PAM) imaging. The minimum bounding rectangle of the segmented region is used in this method to dynamically update the growing threshold for optimal segmentation. Phantom images and PAM imaging results of normal porcine RPE are applied to demonstrate the effectiveness of the segmentation. The method realizes accurate segmentation of RPE cells and also provides the basis for quantitative analysis of cell features such as cell area and component content, which can have potential applications in studying RPE cell functions for PAM imaging.OCIS Retinal pigment epithelium (RPE) is a monolayer of pigmented cells located between photoreceptor outer segments and Bruch's membrane. RPE has the function of transportation of nutrients and metabolic products between photoreceptors and blood [1,2] . Thus, it plays an important role in the maintenance of retinal homeostasis and is implicated in a majority of retinal diseases due to the failure of RPE functions [3][4][5] . An imaging method to examine RPE cells for retinal pathological analysis is needed.Photoacoustic microscopy (PAM) is a noninvasive and label-free three-dimensional imaging modality based on the optical absorption property of biological tissues [6][7][8][9] , which has proved its capacity for in vivo imaging of blood vessels and RPE in fundus [10][11][12][13] . To achieve the cellular or subcellular imaging level, PAM systems have been developed to achieve micrometer or submicrometer resolution and used for imaging red blood cells and pigment cells [7,14,15] . Since the photoacoustic (PA) signals of RPE are mostly generated from the strong light absorption of the pigment components, especially the melanin, the PAM can display the cell morphology, and more importantly, indicate the melanin content in cells [16] , which is highly correlated with aging and retinal diseases such as age-related macular degeneration (AMD) [5,17] . To acquire the RPE cell features such as morphology and component content, accurate cell detection and segmentation in cellular images are important. In previous work, several semi-automatic and automatic cell segmentation methods have been used for cell evaluation, and they acquired remarkable results in the images of corneal endothelium, photoreceptor, and RPE cells [18][19][20][21][22][23][24] , using two-photon fluorescence microscopy, confocal microscopy, and adaptive optics retinal imaging. As these imaging results cannot provide melanin content information of RPE cells, an automatic RPE cell segmentation algorithm for PAM imaging is necessary.In our previous work [15] , RPE cells were imaged using dual modal PAM and optical coherence tomography (OCT). An ordinary region growing method was applied to segment RPE cells in PAM images by utilizing the connectivity of the relatively similar signal intensities within each cell. But the ordinary region growing method is limited as static predefin...