Precise segmentation of the intraretinal layers is as important as the development of the hardware. With the advancement of the hardware in the era of spectral domain OCT, the axial resolution can be as high as 1 to 2 µm.8,9,33–38 Wojtkowski et al. developed an ultra-high resolution OCT with a resolution of 2 µm for retinal scanning with a limited intraretinal layer segmentation.39 Currently commercial OCT devices, including the Spectralis (Heidelberg Engineering, Dossenheim, Germany), Cirrus (Carl Zeiss Meditec, Dublin, CA, USA), RTVue-100 (Optovue, Meridianville, AL,. USA) and others, have achieved approximately 5 µm axial resolution.40–42 There are two commercially available ultra-high resolution OCT devices with an axial resolution of approximately 3 µm, including the Biotigen SD-OCT (Bioptigen Inc., Research Triangle Park, NC)43,44 and the Copernicus HR SOCT (Optopol Technology SA, Zawiercie, Poland).45 With these devices, the quality of retinal OCT images have been dramatically improved compared to time-domain OCT devices, such as the Stratus OCT (Carl Zeiss Meditec, Dublin, CA). In general, the recent developments of commercial devices demonstrate a trend of improving axial resolution. We have developed a 2-µm resolution OCT device that allows the visualization of fine details of the retinal structure.12 Logically, the next step to undertake is the automatic segmentation of the high-resolution OCT images obtained with this particular device. Most algorithms within the commercial devices only provide thickness information for a small number of retinal layers, such as the RNFL and the macula. These commercial algorithms are normally not accessible due to its proprietary nature, forcing the development of independent custom-built softwares.46 For example, Cabrera DeBuc et al. developed a custom-built software for extracting up to six intraretinal layers (including RPE) from Stratus OCT images.16,17 Several OCT prototypes with ultra-high resolution47–49 have demonstrated excellent hardware setups, but image processing has lagged behind. The lagging image processing may limit the use of advanced OCT devices with higher resolution, especially at the prototype stage. In the present study, the manual method for segmenting up to nine intraretinal layers in a small area nasally from the fovea was tested as a first step prior to future clinical studies and further development of robust automatic segmentation software. As a matter of fact, our preliminary analysis pointed the main issues that need to be addressed to obtain a fully automatic segmentation of the overall nine layers manually extracted using UHR-OCT. Even though the results presented were mainly based on manual segmentation of UHR-OCT images and only a subset of eyes was automatically segmented for comparison purposes, the main determination was to establish the repeatability of measurements by visual inspection using UHR-OCT. The agreement demonstrated in the comparison between manual and automatic segmentation methods in the subset of data may provide information on further development of the automatic segmentation software. A more practical interface and robust algorithm to segment all intraretinal layers and handle the large quantities of measured raw data generated by our system along with the associated substantial processing are certainly required, and it is currently under development.