Multi-fusion strategies for deep learning artery and vein segmentation in OCT angiography

Mansour Abtahi; David Le; Jennifer I. Lim; Xincheng Yao

doi:10.1117/12.2646724

14 March 2023 Multi-fusion strategies for deep learning artery and vein segmentation in OCT angiography

Mansour Abtahi, David Le, Jennifer I. Lim, Xincheng Yao

Proceedings Volume 12360, Ophthalmic Technologies XXXIII; 123600S (2023) https://doi.org/10.1117/12.2646724
Event: SPIE BiOS, 2023, San Francisco, California, United States

Conference Poster

Abstract

A convolutional neural network (CNN) with multimodal fusion options was developed for artery-vein (AV) segmentation in OCT angiography (OCTA). We quantitatively evaluated multimodal architectures with early and late OCT-OCTA fusions, compared to the unimodal architectures with OCT-only and OCTA-only inputs. OCT-only architecture is limited for segmentation of large AV branches. The OCTA-only architecture, early OCT-OCTA fusion architecture, and late OCT-OCTA fusion architecture provide competitive performances for AV segmentation with further details. Compared to OCTA-only architecture, the late fusion architecture is slightly better, while the early fusion architecture is slightly worse.

Citation Download Citation

Mansour Abtahi, David Le, Jennifer I. Lim, and Xincheng Yao "Multi-fusion strategies for deep learning artery and vein segmentation in OCT angiography", Proc. SPIE 12360, Ophthalmic Technologies XXXIII, 123600S (14 March 2023); https://doi.org/10.1117/12.2646724

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