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2024 | OriginalPaper | Buchkapitel

CBAV-Loss: Crossover and Branch Losses for Artery-Vein Segmentation in OCTA Images

verfasst von : Zetian Zhang, Xiao Ma, Zexuan Ji, Na Su, Songtao Yuan, Qiang Chen

Erschienen in: Pattern Recognition and Computer Vision

Verlag: Springer Nature Singapore

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Abstract

Obvious errors still exist in the segmentation of artery-vein (AV) in retinal optical coherence tomography angiography (OCTA) images, especially near crossover and branch points. It is believed that these errors occur because the existed segmentation method cannot effectively identify the crossover and branch points of AV. In this study, we proposed a Crossover Loss and a Branch Loss (CBAV-Loss), which are two novel structure-preserving loss functions. By restricting the crossover and branch points of arteries and veins, the segmentation accuracy can be improved by correcting the segmentation errors near the crossover and branch points. The experimental results on a manually annotated AV dataset with 400 OCT and OCTA cubes demonstrate that the crossover and branch losses can effectively reduce errors for AV segmentation and preserve vascular connectivity to a certain extent.

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Titel: CBAV-Loss: Crossover and Branch Losses for Artery-Vein Segmentation in OCTA Images
verfasst von: Zetian Zhang
Xiao Ma
Zexuan Ji
Na Su
Songtao Yuan
Qiang Chen
Verlag: Springer Nature Singapore
Buch: Pattern Recognition and Computer Vision
Print ISBN: 978-981-9985-57-9

Electronic ISBN: 978-981-9985-58-6

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-99-8558-6_5

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