SpecstatOR: Speckle statistics-based iOCT Segmentation Network for Ophthalmic Surgery
This work addresses the need for real-time, accurate segmentation in ophthalmic surgery, offering a domain-specific solution that is incremental by combining statistical analysis with deep learning.
The paper tackled the problem of segmenting intraoperative Optical Coherence Tomography (iOCT) images in ophthalmic surgery by leveraging speckle pattern statistics, resulting in enhanced segmentation accuracy on unseen data without manual labeling.
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.