The emergence of robotics could enable ophthalmic microsurgical procedures that were previously not feasible due to the precision limits of manual delivery, for example, targeted subretinal injection. Determining the distance between the needle tip, the internal limiting membrane (ILM), and the retinal pigment epithelium (RPE) both precisely and reproducibly is required for safe and successful robotic retinal interventions. Recent advances in intraoperative optical coherence tomography (iOCT) have opened the path for 4D image-guided surgery by providing near video-rate imaging with micron-level resolution to visualize retinal structures, surgical instruments, and tool-tissue interactions. In this work, we present a novel pipeline to precisely estimate the distance between the injection needle and the surface boundaries of two retinal layers, the ILM and the RPE, from iOCT volumes. To achieve high computational efficiency, we reduce the analysis to the relevant area around the needle tip. We employ a convolutional neural network (CNN) to segment the tool surface, as well as the retinal layer boundaries from selected iOCT B-scans within this tip area. This results in the generation and processing of 3D surface point clouds for the tool, ILM and RPE from the B-scan segmentation maps, which in turn allows the estimation of the minimum distance between the resulting tool and layer point clouds. The proposed method is evaluated on iOCT volumes from ex-vivo porcine eyes and achieves an average error of 9.24 µm and 8.61 µm measuring the distance from the needle tip to the ILM and the RPE, respectively. The results demonstrate that this approach is robust to the high levels of noise present in iOCT B-scans and is suitable for the interventional use case by providing distance feedback at an average update rate of 15.66 Hz.

中文翻译：

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使用术中 4D OCT 进行机器人视网膜下注射的层距离估计的实时工具

机器人技术的出现可以实现以前由于手动输送的精度限制而无法实现的眼科显微外科手术，例如靶向视网膜下注射。安全、成功的机器人视网膜干预需要精确且可重复地确定针尖、内界膜 (ILM) 和视网膜色素上皮 (RPE) 之间的距离。术中光学相干断层扫描 (iOCT) 的最新进展通过提供微米级分辨率的近视频速率成像来可视化视网膜结构、手术器械和工具-组织相互作用，为 4D 图像引导手术开辟了道路。在这项工作中，我们提出了一种新颖的管道，可以根据 iOCT 体积精确估计注射针与两个视网膜层（ILM 和 RPE）表面边界之间的距离。为了实现高计算效率，我们减少了对针尖周围相关区域的分析。我们采用卷积神经网络 (CNN) 来分割工具表面，以及来自该尖端区域内选定的 iOCT B 扫描的视网膜层边界。这导致从 B 扫描分割图生成和处理工具、ILM 和 RPE 的 3D 表面点云，从而可以估计生成的工具和图层点云之间的最小距离。该方法对离体猪眼的 iOCT 体积进行评估，测量针尖到 ILM 和 RPE 的距离，平均误差分别为 9.24 µm和 8.61 µm 。结果表明，该方法对于 iOCT B 扫描中存在的高噪声水平具有鲁棒性，并且通过以 15.66 Hz 的平均更新率提供距离反馈，适合介入使用案例。