This paper presents an innovative approach to intraoperative Optical Coherence Tomography (iOCT) image segmentation in ophthalmic surgery, leveraging statistical analysis of speckle patterns to incorporate statistical pathology-specific prior knowledge. Our findings indicate statistically different speckle patterns within the retina and between retinal layers and surgical tools, facilitating the segmentation of previously unseen data without the necessity for manual labeling. The research involves fitting various statistical distributions to iOCT data, enabling the differentiation of different ocular structures and surgical tools. The proposed segmentation model aims to refine the statistical findings based on prior tissue understanding to leverage statistical and biological knowledge. Incorporating statistical parameters, physical analysis of light-tissue interaction, and deep learning informed by biological structures enhance segmentation accuracy, offering potential benefits to real-time applications in ophthalmic surgical procedures. The study demonstrates the adaptability and precision of using Gamma distribution parameters and the derived binary maps as sole inputs for segmentation, notably enhancing the model's inference performance on unseen data.

通过统计斑点模式的统计分析，本研究提出了一种创新的眼科手术术中光学相干断层扫描图像分割方法，用于利用统计病理学特定先验知识。研究结果表明，视网膜内和视网膜层之间以及手术器械之间存在统计上不同的斑点模式，从而实现了无需手动标注的先前未见数据的分割。该研究通过拟合各种统计分布到光学相干断层扫描数据，实现了不同眼部结构和手术器械的区分。所提出的分割模型旨在基于先前的组织理解改进统计结果，结合统计参数、光与组织相互作用的物理分析以及生物结构指导的深度学习，提高了分割准确性，并在眼科手术实时应用中具有潜在的益处。该研究证明了使用伽马分布参数和导出的二值图作为唯一输入进行分割的适应性和精度，显著提升了模型对未见数据的推理表现。

SpecstatOR: 基于点斑统计的iOCT眼科手术分割网络