  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.
