Lightweight Learning-Based Automatic Segmentation of Subretinal Blebs on Microscope-Integrated Optical Coherence Tomography Images.

Journal Article (Journal Article)

PURPOSE: Subretinal 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. DESIGN: Ex vivo animal study. METHODS: Microscope-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. RESULTS: The 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. CONCLUSION: The 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.

Full Text

Duke Authors

Cited Authors

  • Song, Z; Xu, L; Wang, J; Rasti, R; Sastry, A; Li, JD; Raynor, W; Izatt, JA; Toth, CA; Vajzovic, L; Deng, B; Farsiu, S

Published Date

  • January 2021

Published In

Volume / Issue

  • 221 /

Start / End Page

  • 154 - 168

PubMed ID

  • 32707207

Pubmed Central ID

  • PMC8120705

Electronic International Standard Serial Number (EISSN)

  • 1879-1891

Digital Object Identifier (DOI)

  • 10.1016/j.ajo.2020.07.020

Language

  • eng

Conference Location

  • United States