Existing methods could segment different numbers of layer boundaries (and/or surfaces), i.e., 2 boundaries (Ref. 9 with SD-OCT for posterior retinal layers of human eyes,10 with SD-OCT for AMD of human eyes,11 with SD-OCT for total retinal thickness of human eyes,12 with TD-OCT for total retinal thickness of human eyes,13 with TD-OCT for optic nerve head of human eyes,14 with SD-OCT for NFL thickness of human eyes,15 with SD-OCT for optic nerve head of human eyes), 3 boundaries (Ref. 26 with SD-OCT for AMD of human eyes), 4 boundaries (Ref. 25 with SD-OCT for patients with retinitis pigmentosa), 5 boundaries (Ref. 4 with TD-OCT for retinal layers of human eyes,30 with SD-OCT for retinal layers of human eyes), 6 boundaries (Ref. 18 with SD-OCT for intraretinal layers of rodent eyes,29 with SD-OCT for retinal layers of human eyes), 7 boundaries (Ref. 5 with TD-OCT for retinal layer of human eyes,8 with TD-OCT for retinal layers of human eyes), 8 boundaries (Ref. 23 with SD-OCT for retinal layers of human eyes,32 with TD-OCT for retinal layers of diabetic eyes), 9 boundaries (Ref. 24 with SD-OCT for retinal layers of human eyes,31 with SD-OCT for retinal layers of human eyes), 10 boundaries (Ref. 17 with SD-OCT for intraretinal layers of rat retinas,28 with SD-OCT for mouse retina), and 11 boundaries (Ref. 22 with SD-OCT for retinal layers of human eyes), varying from OCT types (TD-OCT or SD-OCT), purposes (total retinal thickness, optic nerve head, intraretinal, outer retinal, or pathology), and subjects (human or animal). Among the methods segmenting more layer boundaries than the proposed method, Refs. 17 and 28 focused on rat (or mouse) retina, which are different from human retina; as to Refs. 22, 24, and 31, they adopted Cirrus HD-OCT machines (Carl Zeiss Meditec, Inc., Dublin, California), Topcon 3-D OCT-1000 equipment (Topcon Medical Systems, Oakland, New Jersey), and Spectralis OCT system (Heidelberg Engineering, Heidelberg, Germany) respectively, which are all commercial systems with better signal-to-noise ratio than our customized system. In this study, as experts are only able to delineate 8 layer boundaries due to image quality, we opt to segment these layer boundaries to demonstrate the effectiveness of the methodology. We believe that the proposed methodology could be easily extended for extraction of more layers when the imaging quality is enhanced to be comparable with that of existing commercial systems.