结合基于特征与基于像素配准的新型OCT拼接流水线
Novel OCT mosaicking pipeline with Feature- and Pixel-based registration
AI总结：
针对OCT图像视野受限及现有拼接方法抗噪抗位移能力差的问题，提出结合基于学习的特征匹配与基于像素配准的通用OCT/OCTA图像拼接流水线，并利用SAM模型无监督验证结果，在精度和计算效率上表现优越。
摘要：
高分辨率光学相干断层扫描（OCT）图像对眼科研究至关重要，但受限于其相对狭窄的视野（FoV）。图像拼接是一种通过对齐多张重叠图像以获取更大FoV的技术。当前的拼接流水线通常难以处理输入子视野之间的严重噪声和巨大位移。在本文中，我们提出了一种用于拼接多视角OCT/OCTA en face投影图像的通用流水线。该方法结合了基于学习的特征匹配和稳健的基于像素的配准优势，以有效对齐多张图像。此外，我们推进了已训练的基础模型Segment Anything Model（SAM）的应用，以无监督方式验证拼接结果。我们使用内部数据集和大型公开数据集验证了流水线的有效性，该方法在准确性和计算效率上均展现出优越性能。我们还在 https://github.com/MedICL-VU/OCT-mosaicking 公开发布了图像拼接评估工具及相应流水线。
英文摘要：
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}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
