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

• URL: http://arxiv.org/abs/2311.13052v2
• Date: Thu, 25 Jul 2024 19:44:43 GMT
• Title: Novel OCT mosaicking pipeline with Feature- and Pixel-based registration
• Authors: Jiacheng Wang, Hao Li, Dewei Hu, Yuankai K. Tao, Ipek Oguz,
• Abstract summary: We propose a versatile pipeline for stitching multi-view OCT/ OCTA textiten face projection images. Our method combines the strengths of learning-based feature matching and robust pixel-based registration to align multiple images effectively. The efficacy of our pipeline is validated using an in-house dataset and a large public dataset.
• Score: 8.22581088888652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}.

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