Significance: Subretinal injection is an effective way of delivering transplant genes and cells to treat many degenerative retinal diseases. However, the technique requires high-dexterity and microscale precision of experienced surgeons, who have to overcome the physiological hand tremor and limited visualization of the subretinal space. Aim: To automatically guide the axial motion of microsurgical tools (i.e., a subretinal injector) with microscale precision in real time using a fiber-optic common-path swept-source optical coherence tomography distal sensor. Approach: We propose, implement, and study real-time retinal boundary tracking of A-scan optical coherence tomography (OCT) images using a convolutional neural network (CNN) for automatic depth targeting of a selected retinal boundary for accurate subretinal injection guidance. A simplified 1D U-net is used for the retinal layer segmentation on A-scan OCT images. A Kalman filter, combining retinal boundary position measurement by CNN and velocity measurement by cross correlation between consecutive A-scan images, is applied to optimally estimate the retinal boundary position. Unwanted axial motions of the surgical tools are compensated by a piezoelectric linear motor based on the retinal boundary tracking. Results: CNN-based segmentation on A-scan OCT images achieves the mean unsigned error (MUE) of ∼3 pixels (8.1 μm) using an ex vivo bovine retina model. GPU parallel computing allows real-time inference (∼2 ms) and thus real-time retinal boundary tracking. Involuntary tremors, which include low-frequency draft in hundreds of micrometers and physiological tremors in tens of micrometers, are compensated effectively. The standard deviations of photoreceptor (PR) and choroid (CH) boundary positions get as low as 10.8 μm when the depth targeting is activated. Conclusions: A CNN-based common-path OCT distal sensor successfully tracks retinal boundaries, especially the PR/CH boundary for subretinal injection, and automatically guides the tooltip’s axial position in real time. The microscale depth targeting accuracy of our system shows its promising possibility for clinical application.

中文翻译：

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基于 CNN 的 CP-OCT 传感器与视网膜下注射器集成，用于视网膜边界跟踪和注射引导

意义：视网膜下注射是递送移植基因和细胞治疗多种退行性视网膜疾病的有效方法。然而，该技术需要有经验的外科医生的高灵巧性和微尺度精度，他们必须克服生理性手颤和视网膜下空间的有限可视化。目的：使用光纤共路径扫频源光学相干断层扫描远端传感器，实时以微尺度精度自动引导显微外科工具（即视网膜下注射器）的轴向运动。方法：我们提出、实施和研究 A 扫描光学相干断层扫描 (OCT) 图像的实时视网膜边界跟踪，使用卷积神经网络 (CNN) 自动深度瞄准选定的视网膜边界，以实现准确的视网膜下注射引导。简化的 1D U-net 用于 A 扫描 OCT 图像上的视网膜层分割。卡尔曼滤波器结合了 CNN 的视网膜边界位置测量和连续 A 扫描图像之间互相关的速度测量，用于最优估计视网膜边界位置。手术工具的不需要的轴向运动由基于视网膜边界跟踪的压电线性马达补偿。结果：基于 CNN 的 A 扫描 OCT 图像分割使用离体牛视网膜模型实现了约 3 个像素 (8.1 μm) 的平均无符号误差 (MUE)。GPU 并行计算允许实时推理（~2 毫秒），从而实时视网膜边界跟踪。包括数百微米的低频气流和数十微米的生理性震颤在内的无意识震颤得到有效补偿。当深度瞄准被激活时，感光器 (PR) 和脉络膜 (CH) 边界位置的标准偏差低至 10.8 μm。结论：基于 CNN 的公共路径 OCT 远端传感器成功跟踪视网膜边界，尤其是视网膜下注射的 PR/CH 边界，并实时自动引导工具提示的轴向位置。我们系统的微尺度深度靶向精度显示了其临床应用的前景。