ArticleAmerican journal of ophthalmology2021
Lightweight Learning-Based Automatic Segmentation of Subretinal Blebs on Microscope-Integrated Optical Coherence Tomography Images.
Article in American journal of ophthalmology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 9 citations in OpenAlex.
- New Directions for Ophthalmic OCT - Handhelds, Surgery, and Robotics.Translational vision science & technology · 2025Review
- Microscope-Integrated OCT-Guided Volumetric Measurements of Subretinal Blebs Created by a Suprachoroidal Approach.Translational vision science & technology · 2021Article
- Intraoperative Retinal Changes May Predict Surgical Outcomes After Epiretinal Membrane Peeling.Translational vision science & technology · 2021Article
- Intraoperative OCT-Guided Volumetric Measurements of Subretinal Therapy Delivery in Humans.Journal of vitreoretinal diseasesArticle
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 2 countries.
Funding
Abstract
purposeSubretinal injections of therapeutics are commonly used to treat ocular diseases. Accurate dosing of therapeutics at target locations is crucial but difficult to achieve using subretinal injections due to leakage, and there is no method available to measure the volume of therapeutics successfully administered to the subretinal location during surgery. Here, we introduce the first automatic method for quantifying the volume of subretinal blebs, using porcine eyes injected with Ringer's lactate solution as samples.
designEx vivo animal study.
methodsMicroscope-integrated optical coherence tomography was used to obtain 3D visualization of subretinal blebs in porcine eyes at Duke Eye Center. Two different injection phases were imaged and analyzed in 15 eyes (30 volumes), selected from a total of 37 eyes. The inclusion/exclusion criteria were set independently from the algorithm-development and testing team. A novel lightweight, deep learning-based algorithm was designed to segment subretinal bleb boundaries. A cross-validation method was used to avoid selection bias. An ensemble-classifier strategy was applied to generate final results for the test dataset.
resultsThe algorithm performs notably better than 4 other state-of-the-art deep learning-based segmentation methods, achieving an F1 score of 93.86 ± 1.17% and 96.90 ± 0.59% on the independent test data for entry and full blebs, respectively.
conclusionThe proposed algorithm accurately segmented the volumetric boundaries of Ringer's lactate solution delivered into the subretinal space of porcine eyes with robust performance and real-time speed. This is the first step for future applications in computer-guided delivery of therapeutics into the subretinal space in human subjects.
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