Corneal transplantation is the primary treatment for irreversible corneal diseases, but due to limited donor availability, bioengineered corneal equivalents are being developed as a solution, with biocompatibility, structural integrity, and physical function considered key factors. Since conventional evaluation methods may not fully capture the complex properties of the cornea, there is a need for advanced imaging and assessment techniques. In this study, we proposed a deep learning-based automatic segmentation method for transplanted bioengineered corneal equivalents using optical coherence tomography to achieve a highly accurate evaluation of graft integrity and biocompatibility. Our method provides quantitative individual thickness values, detailed maps, and volume measurements of the bioengineered corneal equivalents, and has been validated through 14 days of monitoring. Based on the results, it is expected to have high clinical utility as a quantitative assessment method for human keratoplasties, including automatic opacity area segmentation and implanted graft part extraction, beyond animal studies.

角膜移植是不可逆性角膜疾病的主要治疗方法，但由于供体可用性有限，正在开发生物工程角膜等效物作为解决方案，其中生物相容性、结构完整性和物理功能被认为是关键因素。由于传统的评估方法可能无法完全捕捉角膜的复杂特性，因此需要先进的成像和评估技术。在这项研究中，我们提出了一种基于深度学习的自动分割方法，用于使用光学相干断层扫描对移植物的生物工程角膜等效物进行高精度评估，以实现对移植物完整性和生物相容性的高精度评估。我们的方法提供了生物工程角膜等效物的定量个体厚度值、详细图和体积测量值，并已通过 14 天的监测得到验证。基于结果，预计它作为人类角膜移植术的定量评估方法将具有很高的临床实用性，包括动物研究之外的自动混浊区域分割和植入移植物部分提取。