The overarching goal of this letter is to demonstrate the feasibility of using optical coherence tomography (OCT) to guide a robotic system to extract lens fragments from ex vivo pig eyes. A convolutional neural network (CNN) was developed to semantically segment four intraocular structures (lens material, capsule, cornea, and iris) from OCT images. The neural network was trained on images from ten pig eyes, validated on images from eight different eyes, and tested on images from another ten eyes. This segmentation algorithm was incorporated into the Intraocular Robotic Interventional Surgical System (IRISS) to realize semi-automated detection and extraction of lens material. To demonstrate the system, the semi-automated detection and extraction task was performed on seven separate ex vivo pig eyes. The developed neural network exhibited 78.20% for the validation set and 83.89% for the test set in mean intersection over union metrics. Successful implementation and efficacy of the developed method were confirmed by comparing the preoperative and postoperative OCT volume scans from the seven experiments.

中文翻译：

---

使用深度学习的 OCT 图像语义分割通过手术机器人半自动提取镜片碎片 - 体外动物模型的实验结果

这封信的首要目标是证明使用光学相干断层扫描 (OCT) 引导机器人系统从离体猪眼。卷积神经网络 (CNN) 被开发用于从 OCT 图像中语义分割四个眼内结构（镜片材料、囊、角膜和虹膜）。神经网络在十只猪眼睛的图像上进行训练，在八只不同眼睛的图像上进行验证，并在另外十只眼睛的图像上进行测试。该分割算法被整合到眼内机器人介入手术系统（IRISS）中，实现了晶状体材料的半自动检测和提取。为了演示该系统，半自动检测和提取任务在七个独立的离体猪眼。所开发的神经网络在联合指标的平均交集上显示出 78.20% 的验证集和 83.89% 的测试集。通过比较七个实验的术前和术后 OCT 体积扫描，证实了所开发方法的成功实施和有效性。