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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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SpecstatOR: speckle statistics-based iOCT segmentation network for ophthalmic surgery.

Kristina Mach¹, Hessam Roodaki², Michael Sommersperger¹

¹Chair for Computer Aided Medical Procedures and Augmented Reality, (I16), TUM School of Computation, Information and Technology, Technische Universitat Munchen, Boltzmannstr. 3, 85748 Garching, Germany.

Biomedical Optics Express

|March 2, 2026

Summary

This summary is machine-generated.

This study presents a novel intraoperative optical coherence tomography (iOCT) segmentation method. It uses tissue speckle patterns for accurate retinal layer and instrument differentiation, improving surgical precision.

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Area of Science:

Ophthalmology
Medical Imaging
Biomedical Engineering

Background:

Intraoperative optical coherence tomography (iOCT) is crucial for ophthalmic surgery.
Accurate segmentation of retinal layers and surgical instruments is challenging.
Current segmentation methods often rely on shape and intensity, limiting robustness.

Purpose of the Study:

To develop a robust iOCT segmentation approach.
To differentiate retinal layers and surgical instruments effectively.
To overcome limitations of existing segmentation techniques.

Main Methods:

Utilizing speckle patterns from tissue and tool scattering properties.
Defining segmentation based on refractive index and structural composition.
Training the model on tissue-specific characteristics for enhanced robustness.

Main Results:

The approach successfully differentiates retinal layers and instruments.
It demonstrates robustness across different devices and anatomical variations.
Reduced dependency on shape and intensity compared to traditional methods.

Conclusions:

The proposed method offers a more reliable iOCT segmentation.
It enhances surgical precision by accurately identifying critical structures.
This technique minimizes the need for retraining, improving efficiency.