Self-calibrating Intelligent OCT-SLO System
Mayank Goswami

TL;DR
This paper introduces a novel AI-driven self-calibrating OCT-SLO system that automates optical alignment and contrast adjustment, achieving high spatial resolution and reducing manual intervention for ocular imaging.
Contribution
The work presents a unique sample-independent 3D self-calibration method combined with fully automatic AI-driven optical alignment for OCT-SLO systems, enhancing imaging speed and accuracy.
Findings
AI approach is 200% faster than classical methods.
Achieves spatial resolution of 2.41 microns in phantom and 0.76 microns in mouse retina.
System provides true 3D images across multiple spectrums for dynamic profiling.
Abstract
A unique sample independent 3D self calibration methodology is tested on a unique optical coherence tomography and multi-spectral scanning laser ophthalmoscope (OCT-SLO) hybrid system. Operators visual cognition is replaced by computer vision using the proposed novel fully automatic AI-driven system design. Sample specific automatic contrast adjustment of the beam is achieved on the pre-instructed region of interest. The AI model deduces infrared, fluorescence, and visual spectrum optical alignment by estimating pre-instructed features quantitatively. The tested approach, however, is flexible enough to utilize any apt AI model. Relative comparison with classical signal-to-noise-driven automation is shown to be 200 percent inferior and 130 percent slower than the AI-driven approach. The best spatial resolution of the system is found to be (a) 2.41 microns in glass bead eye phantom, 0.76…
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Taxonomy
TopicsOptical Coherence Tomography Applications
