Automated image processing pipeline for adaptive optics scanning light ophthalmoscopy |
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| Authors: | Alexander E. Salmon Robert F. Cooper Min Chen Brian Higgins Jenna A. Cava Nickolas Chen Hannah M. Follett Mina Gaffney Heather Heitkotter Elizabeth Heffernan Taly Gilat Schmidt Joseph Carroll |
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| Affiliation: | 1.Cell Biology, Neurobiology, and Anatomy, Medical College of Wisconsin, Milwaukee, WI 53226, USA;2.Translational Imaging Innovations, Inc., Hickory, NC 28601, USA;3.Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI 53233, USA;4.Ophthalmology and Visual Sciences, Medical College of Wisconsin, 8701 W. Watertown Plank Rd., Milwaukee, WI 53226, USA;5.Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA |
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| Abstract: | To mitigate the substantial post-processing burden associated with adaptive optics scanning light ophthalmoscopy (AOSLO), we have developed an open-source, automated AOSLO image processing pipeline with both “live” and “full” modes. The live mode provides feedback during acquisition, while the full mode is intended to automatically integrate the copious disparate modules currently used in generating analyzable montages. The mean (±SD) lag between initiation and montage placement for the live pipeline was 54.6 ± 32.7s. The full pipeline reduced overall human operator time by 54.9 ± 28.4%, with no significant difference in resultant cone density metrics. The reduced overhead decreases both the technical burden and operating cost of AOSLO imaging, increasing overall clinical accessibility. |
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