A retinal OCT computer-implemented method for segmenting a test retinal OCT image into a plurality of retinal regions, said regions comprising at least the vitreous, the retinal pigment epithelium and one or more regions selected from the nerve fiber layer, ganglion cell layer, inner plexiform layer, internal limiting membrane, inner nuclear layer, outer plexiform layer, outer nuclear layer, external limiting membrane, inner segments, inner/outer segment junction (I/OS), and outer segments of the photoreceptors, the method comprising: a) providing an atlas probabilistic shape and intensity model derived from a plurality of OCT images generated from control subjects and segmented into a plurality of retinal regions, said regions comprising at least the vitreous, the retinal pigment epithelium and one or more regions selected from the nerve fiber layer, ganglion cell layer, inner plexiform layer, internal limiting membrane, inner nuclear layer, outer plexiform layer, outer nuclear layer, external limiting membrane, inner segments, inner/outer segment junction (I/OS), and outer segments of the photoreceptors; b) aligning the test OCT image with the atlas probabilistic shape and intensity model according to retinal region shape, wherein said aligning comprises (i) constructing a wavelet decomposition of the test image, (ii) detecting at least three regional boundaries, (iii) identifying the foveal pit by reference to the detected boundaries, (iv) placing fiduciary points on each detected boundary at locations equidistant from the foveal pit, and (v) aligning the test image to the model image by aligning the fiduciary points; c) comparing pixels of the aligned test OCT image with the corresponding pixels in the atlas model by intensity, establishing a probable regional fit, and refining regional margins by estimating marginal density distribution with a linear combination of discrete Gaussians (LCDG) to generate an initial regional segmentation map of the test OCT image comprising defined retinal regions; and d) fitting the initial segmentation map with a Markov-Gibbs Random Field model that accounts for spatial information to achieve a final segmentation map comprising said retinal regions. a) providing an atlas probabilistic shape and intensity model derived from a plurality of OCT images generated from control subjects and segmented into a plurality of retinal regions, said regions comprising at least the vitreous, the retinal pigment epithelium and one or more regions selected from the nerve fiber layer, ganglion cell layer, inner plexiform layer, internal limiting membrane, inner nuclear layer, outer plexiform layer, outer nuclear layer, external limiting membrane, inner segments, inner/outer segment junction (I/OS), and outer segments of the photoreceptors; b) aligning the test OCT image with the atlas probabilistic shape and intensity model according to retinal region shape, wherein said aligning comprises (i) constructing a wavelet decomposition of the test image, (ii) detecting at least three regional boundaries, (iii) identifying the foveal pit by reference to the detected boundaries, (iv) placing fiduciary points on each detected boundary at locations equidistant from the foveal pit, and (v) aligning the test image to the model image by aligning the fiduciary points; (i) constructing a wavelet decomposition of the test image, (ii) detecting at least three regional boundaries, (iii) identifying the foveal pit by reference to the detected boundaries, (iv) placing fiduciary points on each detected boundary at locations equidistant from the foveal pit, and (v) aligning the test image to the model image by aligning the fiduciary points; c) comparing pixels of the aligned test OCT image with the corresponding pixels in the atlas model by intensity, establishing a probable regional fit, and refining regional margins by estimating marginal density distribution with a linear combination of discrete Gaussians (LCDG) to generate an initial regional segmentation map of the test OCT image comprising defined retinal regions; and d) fitting the initial segmentation map with a Markov-Gibbs Random Field model that accounts for spatial information to achieve a final segmentation map comprising said retinal regions.