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Updated: Jul 29, 2025

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
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Longitudinal deep network for consistent OCT layer segmentation.

Yufan He1, Aaron Carass1, Yihao Liu1

  • 1Dept. of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.

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|May 19, 2023
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Summary
This summary is machine-generated.

This study introduces a new deep learning method for analyzing optical coherence tomography (OCT) scans. It improves the accuracy of measuring retinal layer thickness in people with multiple sclerosis (PwMS) for better patient monitoring.

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Area of Science:

  • Ophthalmology
  • Neuroscience
  • Medical Imaging

Background:

  • Retinal layer thickness is a key biomarker for multiple sclerosis (MS) progression.
  • Optical coherence tomography (OCT) is used clinically to monitor MS, but patient-level trends are hard to discern due to segmentation variability.
  • Current deep learning methods for OCT segmentation lack longitudinal analysis, limiting their utility for individual patient monitoring.

Purpose of the Study:

  • To develop a novel longitudinal OCT segmentation network.
  • To improve the accuracy and consistency of retinal layer thickness measurements in people with multiple sclerosis (PwMS).
  • To enable more precise patient-specific disease monitoring and treatment planning using OCT data.

Main Methods:

  • Proposed a deep learning network that incorporates longitudinal information from sequential OCT scans.
  • Developed an algorithm for more accurate and consistent retinal layer segmentation.
  • Validated the network's performance on a cohort of PwMS.

Main Results:

  • The proposed longitudinal OCT segmentation network demonstrated enhanced accuracy in measuring retinal layer thickness.
  • The method provided more consistent measurements compared to non-longitudinal approaches.
  • The findings suggest improved potential for patient-level trend identification.

Conclusions:

  • Longitudinal analysis in OCT segmentation significantly improves measurement accuracy and consistency for PwMS.
  • This approach holds promise for personalized disease monitoring and treatment strategies in multiple sclerosis.
  • The developed network offers a valuable tool for clinical application in managing MS progression.