  Image data set from a multi-spectral animal imaging system is 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 AI-based algorithms
in combination are tested for each task to see their compatibility with data
from the combined animal imaging system. Hybridization of AI with optical flow
followed by Homography transformation is shown to be working (correlation
value>0.7) for registration. Resnet50 backbone is shown to be working better
than the famous U-net model for shadow region detection with a loss value of
0.9. A simple-to-implement analytical equation is shown to be working for
brightness manipulation with a 1% increment in mean pixel values and a 77%
decrease in the number of zeros. The proposed equation allows formulating a
constraint optimization problem using a controlling factor {\alpha} for
minimization of number of zeros, standard deviation of pixel value and
maximizing the mean pixel value. For Layer segmentation, the standard U-net
model is used. The AI-Pipeline consists of CNN, Optical flow, RCNN, pixel
manipulation model, and U-net models in sequence. The thickness estimation
process has a 6% error as compared to manual annotated standard data.
