SpecstatOR: Speckle statistics-based iOCT Segmentation Network for Ophthalmic Surgery

TL;DR

This paper introduces SpecstatOR, a novel iOCT segmentation network that uses speckle pattern statistics and deep learning to improve real-time ophthalmic surgery imaging without manual labels.

Contribution

It leverages speckle pattern statistical analysis combined with deep learning to enable accurate, label-free segmentation of ocular structures and surgical tools in iOCT images.

Findings

Speckle patterns differ significantly within retina and between layers.

Gamma distribution parameters effectively differentiate ocular structures.

Model achieves high accuracy on unseen data without manual labeling.

Abstract

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.