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

Novel OCT mosaicking pipeline with Feature- and Pixel-based registration