  High-resolution Optical Coherence Tomography (OCT) images are crucial for
ophthalmology studies but are limited by their relatively narrow field of view
(FoV). Image mosaicking is a technique for aligning multiple overlapping images
to obtain a larger FoV. Current mosaicking pipelines often struggle with
substantial noise and considerable displacement between the input sub-fields.
In this paper, we propose a versatile pipeline for stitching multi-view
OCT/OCTA \textit{en face} projection images. Our method combines the strengths
of learning-based feature matching and robust pixel-based registration to align
multiple images effectively. Furthermore, we advance the application of a
trained foundational model, Segment Anything Model (SAM), to validate
mosaicking results in an unsupervised manner. The efficacy of our pipeline is
validated using an in-house dataset and a large public dataset, where our
method shows superior performance in terms of both accuracy and computational
efficiency. We also made our evaluation tool for image mosaicking and the
corresponding pipeline publicly available at
\url{https://github.com/MedICL-VU/OCT-mosaicking}.
