Keller Brenton, Draelos Mark, Tang Gao, Farsiu Sina, Kuo Anthony N, Hauser Kris, Izatt Joseph A
Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
Department of Mechanical Engineering, Duke University, Durham, NC 27708, USA.
Biomed Opt Express. 2018 May 21;9(6):2716-2732. doi: 10.1364/BOE.9.002716. eCollection 2018 Jun 1.
Ophthalmic procedures demand precise surgical instrument control in depth, yet standard operating microscopes supply limited depth perception. Current commercial microscope-integrated optical coherence tomography partially meets this need with manually-positioned cross-sectional images that offer qualitative estimates of depth. In this work, we present methods for automatic quantitative depth measurement using real-time, two-surface corneal segmentation and needle tracking in OCT volumes. We then demonstrate these methods for guidance of deep anterior lamellar keratoplasty (DALK) needle insertions. Surgeons using the output of these methods improved their ability to reach a target depth, and decreased their incidence of corneal perforations, both with statistical significance. We believe these methods could increase the success rate of DALK and thereby improve patient outcomes.
眼科手术需要在深度上精确控制手术器械，但标准手术显微镜提供的深度感知有限。目前的商用显微镜集成光学相干断层扫描通过手动定位的横截面图像部分满足了这一需求，这些图像提供了深度的定性估计。在这项工作中，我们提出了使用实时双表面角膜分割和OCT体积中的针跟踪进行自动定量深度测量的方法。然后，我们展示了这些方法用于指导深层前板层角膜移植术（DALK）针插入。使用这些方法输出的外科医生提高了到达目标深度的能力，并降低了角膜穿孔的发生率，两者均具有统计学意义。我们相信这些方法可以提高DALK的成功率，从而改善患者的治疗效果。
