School of Electrical and Information Engineering, Tianjin University, Tianjin, China; Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA.
Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA.
Am J Ophthalmol. 2021 Jan;221:154-168. doi: 10.1016/j.ajo.2020.07.020. Epub 2020 Jul 21.
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.
Ex vivo animal study.
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.
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.
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.
眼内药物注射治疗是治疗眼部疾病的常用方法。由于渗漏，准确地将药物剂量投放到靶位是非常重要的，但通过眼内注射很难实现，目前还没有方法可以在手术过程中成功测量递送到眼内下腔的药物体积。在这里，我们介绍了第一个自动量化眼内下腔气泡体积的方法，使用乳酸林格氏液注射的猪眼作为样本。
动物离体研究。
使用集成显微镜的光学相干断层扫描术（OCT）获得在杜克眼中心的猪眼内的眼内下腔气泡的 3D 可视化。在 15 只眼（30 个容积）中对两种不同的注射阶段进行成像和分析，这 15 只眼是从总共 37 只眼中选择的。纳入/排除标准是独立于算法开发和测试团队设定的。设计了一种新的轻量级、基于深度学习的算法来分割眼内下腔气泡边界。使用交叉验证方法避免选择偏差。应用集成分类器策略为测试数据集生成最终结果。
该算法明显优于其他 4 种最先进的基于深度学习的分割方法，在独立测试数据中，入口和完全气泡的 F1 分数分别为 93.86±1.17%和 96.90±0.59%。
所提出的算法能够准确地分割乳酸林格氏液在猪眼内下腔空间中的体积边界，具有强大的性能和实时速度。这是未来在人类受试者中进行计算机引导的眼内下腔药物输送的应用的第一步。
