使用M模式OCT数据进行深前板层角膜移植术（DALK）手术引导的基于拓扑的深度学习分割方法
Topology-based deep-learning segmentation method for deep anterior lamellar keratoplasty (DALK) surgical guidance using M-mode OCT data
AI总结：
针对DALK中M模式OCT信号噪声导致角膜层分割不稳的问题，提出融合拓扑损失与改进网络的深度学习方法，实现上皮和DM的快速精准鲁棒分割。
摘要：
深前板层角膜移植术（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。该手术中的关键步骤是使用大气泡技术将深层基质与Descemet膜（DM）精确分离。为简化该技术中的穿刺进针和气动分离任务，我们此前开发了一种由光学相干断层扫描（OCT）引导、可佩戴在眼部的机器人，该机器人利用来自M模式OCT信号的角膜层实时跟踪进行控制。然而，在操作集成有OCT光纤传感器的针头期间，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，导致角膜层检测粗糙且不准确。为应对这些挑战，我们开发了一种基于拓扑的深度学习分割方法，该方法将拓扑损失函数与改进的网络架构相结合。该方法有效降低了噪声影响，并提高了分割速度、精度和稳定性。使用在体、离体和混合兔眼数据集进行的验证表明，我们的方法优于基于传统损失的技术，可对上皮和DM进行快速、准确且鲁棒的分割，以引导手术。
英文摘要：
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.
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