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An image data set from a multi-spectral animal imaging system was used to address two issues: (a) registering the oscillation in optical coherence tomography (OCT) images due to mouse eye movement and (b) suppressing the shadow region under the thick vessels/structures. Several classical and A.I.-based algorithms, separately and in combination, were tested for each task to determine their compatibility with data from the combined animal imaging system. The hybridization of A.I. with optical flow followed by homography transformation was shown to be effective (correlation value > 0.7) for registration. Resnet50 backbone was shown to be more effective than the famous U-net model for shadow region detection with a loss value of 0.9. A simple-to-implement analytical equation was shown to be effective for brightness manipulation with a 1% increment in mean pixel values and a 77% decrease in the number of zeros. The proposed equation allows the formulation of a constraint optimization problem using a controlling factor <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>α</mi></mrow></semantics></math></inline-formula> for the minimization of the number of zeros, the standard deviation of the pixel values, and maximizing the mean pixel value. For layer segmentation, the standard U-net model was used. The A.I.-Pipeline consists of CNN, optical flow, RCNN, a pixel manipulation model, and U-net models in sequence. The thickness estimation process had a 6% error compared with manually annotated standard data.