Semi-Automated Extraction of Lens Fragments via a Surgical Robot Using Semantic Segmentation of OCT Images with Deep Learning - Experimental Results in ex vivo Animal Model.
Semi-Automated Extraction of Lens Fragments via a Surgical Robot Using Semantic Segmentation of OCT Images with Deep Learning - Experimental Results in ex vivo Animal Model.
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DOI：
10.1109/lra.2021.3072574
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发表时间：
2021-07
影响因子：
5.2
通讯作者：
Rosen J
中科院分区：
文献类型：
--
作者：
Shin C;Gerber MJ;Lee YH;Rodriguez M;Pedram SA;Hubschman JP;Tsao TC;Rosen J
这项工作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睛中提取透镜碎片的可行性。开发了卷积神经网络（CNN），用于从OCT图像中语义分割四种眼内结构（透镜材料、囊膜、角膜和虹膜）。该神经网络在来自10只猪眼睛的图像上进行了训练，在来自8只不同眼睛的图像上进行了验证，并在来自另外10只眼睛的图像上进行了测试。将该分割算法应用于眼内机器人介入手术系统（IRISS），实现了透镜材料的半自动检测和提取。为了证明该系统，在七个单独的离体猪眼上进行半自动检测和提取任务。所开发的神经网络表现出78.20%的验证集和83.89%的测试集的平均交集超过工会度量。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。
The overarching goal of this work 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.