Keratocytes are fibroblast-like cells that maintain the optical clarity and the overall health of the cornea. The ability to measure precisely their density and spatial distribution in the cornea is important for the understanding of corneal healing processes and the diagnostics of some corneal disorders. A novel computerized approach to detection and counting of keratocyte cells from ultra high resolution optical coherence tomography (UHR-OCT) images of the human corneal stroma is presented. The corneal OCT data is first processed using a state-of-the-art despeckling algorithm to reduce the effect of speckle on detection accuracy. A thresholding strategy is then employed to allow for improved delineation of keratocyte cells by suppressing similarly shaped features in the data, followed by a second-order moment analysis to identify potential cell nuclei candidates. Finally, a local extrema strategy is used to refine the candidates to determine the locations and the number of keratocyte cells. Cell density distribution analysis was carried in 3D UHR-OCT images of the human corneal stroma, acquired in-vivo. The cell density results obtained using the proposed novel approach correlate well with previous work on computerized keratocyte cell counting from confocal microscopy images of human cornea.