Leveraging Multimodal Fusion for Enhanced Diagnosis of Multiple Retinal Diseases in Ultra-Wide Octa

Ultra-wide optical coherence tomography angiography (UW-OCTA) is an emerging imaging technique that offers significant advantages over traditional OCTA by providing an exceptionally wide scanning range of up to 24 x 20 mm 2 , covering both the anterior and posterior regions of the retina. However, t...

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Bibliographic Details
Main Authors: Wei, Hao, Shi, Peilun, Bai, Guitao, Zhang, Minqing, Li, Shuangle, Yuan, Wu
Format: Conference Proceeding
Language:English
Subjects:
Angiography
Annotations
Biological system modeling
Image analysis
Open access dataset
Optical coherence tomography
Retina
Ultal-wide Optical coherence tomography angiography
Visualization
Online Access:Request full text
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Summary:Ultra-wide optical coherence tomography angiography (UW-OCTA) is an emerging imaging technique that offers significant advantages over traditional OCTA by providing an exceptionally wide scanning range of up to 24 x 20 mm 2 , covering both the anterior and posterior regions of the retina. However, the currently accessible UW-OCTA datasets suffer from limited comprehensive hierarchical information and corresponding disease annotations. To address this limitation, we have curated the pioneering M3OCTA dataset, which is the first multimodal (i.e., multilayer), multi-disease, and widest field-of-view UW-OCTA dataset. Furthermore, the effective utilization of multi-layer ultra-wide ocular vasculature information from UW-OCTA remains underdeveloped. To tackle this challenge, we propose the first cross-modal fusion framework that leverages multi-modal information for diagnosing multiple diseases. Through extensive experiments conducted on our openly available M3OCTA dataset, we demonstrate the effectiveness and superior performance of our method, both in fixed and varying modalities settings. The construction of the M3OCTA dataset, the first multimodal OCTA dataset encompassing multiple diseases, aims to advance research in the ophthalmic image analysis community.
ISSN:1945-8452
DOI:10.1109/ISBI56570.2024.10635142