Abstract
We present a new framework for fine-scale vessel segmentation from fundus images through registration and segmentation of corresponding fluorescein angiography (FA) images. In FA, fluorescent dye is used to highlight the vessels and increase their contrast. Since these highlights are temporally dispersed among multiple FA frames, we first register the FA frames and aggregate the per-frame segmentations to construct a detailed vessel mask. The constructed FA vessel mask is then registered to the fundus image based on an initial fundus vessel mask. Postprocessing is performed to refine the final vessel mask. Registration of FA frames, as well as registration of FA vessel mask to the fundus image, are done by similar hierarchical coarse-to-fine frameworks, both comprising rigid and non-rigid registration. Two CNNs with identical network structures, both trained on public datasets but with different settings, are used for vessel segmentation. The resulting final vessel segmentation contains fine-scale, filamentary vessels extracted from FA and corresponding to the fundus image. We provide quantitative evaluation as well as qualitative examples which support the robustness and the accuracy of the proposed method.
This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government (MoE) (NRF-2018R1D1A1A09083241 and NRF-2019R1F1A1063656).
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Noh, K.J., Park, S.J., Lee, S. (2019). Fine-Scale Vessel Extraction in Fundus Images by Registration with Fluorescein Angiography. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11764. Springer, Cham. https://doi.org/10.1007/978-3-030-32239-7_86
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