1. A method of training an algorithm to identify automatically a blood vessel in a three-dimensional optical coherence tomography image, comprising: (A) obtaining at least one fundus image generated from A-scans of a plurality of three-dimensional optical coherence tomographic images containing said vessel; (B) labeling each pixel in said fundus image as vessel containing or non vessel containing; (C) generating at least one training data set by selecting randomly from said plurality a first number of said A-scans corresponding to said respective vessel containing pixels and a second number of said A-scans corresponding to said respective non vessel containing pixels; (D) for each A-scan in said training data set, extracting features comprising at least one two-dimensional feature and at least one one-dimensional feature; (E) generating an ensemble classifier by iteratively comparing said features with said corresponding labeled pixels, such that said ensemble classifier is trained to identify automatically said vessel.