Conference proceeding
Dynamic retinal blood flow analysis: Heartbeat-correlated artery and vein identification in laser speckle flowgraphy
Vol.13410, pp.1341009-1341009-8
Progress in Biomedical Optics and Imaging
04/02/2025
DOI: 10.1117/12.3046777
Abstract
Laser speckle flowgraphy (LSFG) is a non-invasive technique for evaluating retinal and choroidal blood flow, estimating flow velocity using the mean blur rate (MBR) from speckle pattern changes. An LSFG video consists of 120 time frames over approximately four heartbeats. The traditional analysis tool, total retinal artery and vein analysis (TRAVA), requires manual identification of the optic disc and setting up regions of interest (i.e., placing an annulus grid). This manual process is often tedious and subjective. In this study, we introduce an optimized deep-learning framework (utilizing nnU-Net, version 2.1) using temporal blood flow variations across heartbeats as multichannel inputs to automatically identify retinal arteries and veins in LSFG. For model training, an ophthalmologist manually traced arteries and veins in color fundus photographs, which were then converted into LSFG labels by an LSFG expert. Data from 40 patients with both LSFG and fundus photographs were used: 30 for training and ten for testing. The proposed model achieved Dice coefficients of 0.78 ± 0.06 for arteries and 0.79 ± 0.06 for veins, significantly outperforming the state-of-the-art AutoMorph’s core approach (retrained by the same data), which had Dice coefficients of 0.61 ± 0.16 and 0.63 ± 0.07, respectively (p-values 0.01). Our method also yielded significantly higher recall and F1 scores in identifying artery and vein segments at the optic disc compared to TRAVA (p-values 0.01). For vessel segment counting and blood flow quantification at the optic disc annulus grid, the proposed method showed no significant differences from manual tracing (p-values ≫ 0.05). Overall, nnU-Net with normalized LSFG time frames as multi-channel inputs can automatically identify arteries and veins without manual intervention. This automated method enables the use of LSFG for larger datasets, potentially increasing its adoption and utility.
Details
- Title: Subtitle
- Dynamic retinal blood flow analysis: Heartbeat-correlated artery and vein identification in laser speckle flowgraphy
- Creators
- Noriyoshi Takahashi - The VA Ctr. for the Prevention and Treatment of Visual Loss (United States)Jui-Kai Wang - The VA Ctr. for the Prevention and Treatment of Visual Loss (United States)Edward F. Linton - The VA Ctr. for the Prevention and Treatment of Visual Loss (United States)Noor-Us-Sabah Ahmad - University of IowaMona K. Garvin - The VA Ctr. for the Prevention and Treatment of Visual Loss (United States)Randy H. Kardon - The VA Ctr. for the Prevention and Treatment of Visual Loss (United States)
- Contributors
- Barjor S. Gimi (Editor) - University of Massachusetts Chan Medical SchoolAndrzej Krol (Editor) - SUNY Upstate Medical University
- Resource Type
- Conference proceeding
- Publication Details
- Vol.13410, pp.1341009-1341009-8
- Publisher
- SPIE
- Series
- Progress in Biomedical Optics and Imaging
- DOI
- 10.1117/12.3046777
- ISSN
- 1605-7422
- Grant note
- Department of Veteran Affairs (VA) Rehabilitation Research and Development (RRD): I50RX003002, RRD I01RX003797 National Institutes of Health (NIH): R01EY031544
This study was supported, in part, by the Department of Veteran Affairs (VA) Rehabilitation Research and Development (RR&D) I50RX003002, RR&D I01RX003797, and National Institutes of Health (NIH) R01EY031544.
- Language
- English
- Date published
- 04/02/2025
- Academic Unit
- Electrical and Computer Engineering; Iowa Neuroscience Institute; Ophthalmology and Visual Sciences
- Record Identifier
- 9984813194202771
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