标题： 基于拓扑的深度学习分割方法，用于使用M模式OCT数据的深前层角膜移植术（DALK）手术指导 显示英文标题

标题： Topology-based deep-learning segmentation method for deep anterior lamellar keratoplasty (DALK) surgical guidance using M-mode OCT data

摘要： 深前层角膜移植术（DALK）是一种用于治疗角膜基质疾病的部分厚度角膜移植手术。 此手术中的关键步骤是使用大泡技术精确分离深层基质和Descemet膜（DM）。 为了简化该技术中的针头插入和气动分离任务，我们之前开发了一种光学相干断层扫描（OCT）引导的、可安装在眼睛上的机器人，该机器人使用从M模式OCT信号中实时跟踪角膜层来进行控制。 然而，在操作集成OCT光纤传感器的针头时，信号噪声和不稳定性阻碍了传统深度学习分割方法的性能，导致角膜层的检测粗糙且不准确。 为了解决这些挑战，我们开发了一种基于拓扑结构的深度学习分割方法，该方法将拓扑损失函数与修改后的网络架构相结合。 这种方法有效减少了噪声的影响，并提高了分割速度、精度和稳定性。 使用活体、离体和混合兔眼数据集进行验证表明，我们的方法优于传统的基于损失的技术，能够快速、准确且稳健地分割上皮和DM以指导手术。 显示英文摘要

摘要： Deep Anterior Lamellar Keratoplasty (DALK) is a partial-thickness corneal transplant procedure used to treat corneal stromal diseases. A crucial step in this procedure is the precise separation of the deep stroma from Descemet's membrane (DM) using the Big Bubble technique. To simplify the tasks of needle insertion and pneumo-dissection in this technique, we previously developed an Optical Coherence Tomography (OCT)-guided, eye-mountable robot that uses real-time tracking of corneal layers from M-mode OCT signals for control. However, signal noise and instability during manipulation of the OCT fiber sensor-integrated needle have hindered the performance of conventional deep-learning segmentation methods, resulting in rough and inaccurate detection of corneal layers. To address these challenges, we have developed a topology-based deep-learning segmentation method that integrates a topological loss function with a modified network architecture. This approach effectively reduces the effects of noise and improves segmentation speed, precision, and stability. Validation using in vivo, ex vivo, and hybrid rabbit eye datasets demonstrates that our method outperforms traditional loss-based techniques, providing fast, accurate, and robust segmentation of the epithelium and DM to guide surgery.