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
https://github.com/MedICL-VU/OCT-mosaicking.

本文提出了一种多视图 OCT/OCTA 面投影图像拼接的通用流程，通过结合基于学习的特征匹配和健壮的基于像素的配准方法，有效地对齐多个图像。此外，我们还将基于训练的基础模型 Segment Anything Model (SAM) 的应用推进到了无监督方式验证拼接结果。通过对内部数据集和大型公共数据集的验证，我们的方法在准确性和计算效率方面都显示出优越的性能。同时，我们提供了图像拼接的评估工具和相应的流程公开下载。