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Determining Gender-Based Differences in Retinal and Choroidal Thickness in Underweight Individuals via Swept-Source Optical Coherence Tomography
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Determining Gender-Based Differences in Retinal and Choroidal Thickness in Underweight Individuals via Swept-Source Optical Coherence Tomography

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Error rate of automated choroidal segmentation using swept-source optical coherence tomography.

Mingui Kong1, Doo Ri Eo1, Gyule Han1

  • 1Department of Ophthalmology, School of Medicine, Samsung Medical Center, Sungkyunkwan University, Seoul, South Korea.

Acta Ophthalmologica
|February 27, 2016
PubMed
Summary

Automated choroidal segmentation has a high error rate, especially in cases with choroidal abnormalities. Frame averaging did not significantly reduce these errors, indicating a need for improved technology.

Keywords:
automated choroidal segmentationswept-source optical coherence tomography

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Biomedical Engineering

Background:

  • Accurate choroidal segmentation is crucial for diagnosing and monitoring various eye conditions.
  • Automated segmentation tools aim to improve efficiency and consistency in clinical practice.
  • Optical coherence tomography (OCT) is a key imaging modality for visualizing the choroid.

Purpose of the Study:

  • To evaluate the accuracy of automated choroidal segmentation using swept-source OCT.
  • To determine the impact of frame averaging on the error rate of automated choroidal segmentation.
  • To identify factors influencing the performance of automated choroidal segmentation algorithms.

Main Methods:

  • Horizontal B-scans of the fovea were acquired using swept-source OCT in patients with diverse retinochoroidal disorders.
  • Images were categorized into four groups: normal from fellow eyes (NF), normal from pathologic eyes (NP), retinal abnormality (R), and retinochoroidal abnormality (RC).
  • Automated choroidal segmentation was performed using the OCT device's built-in software, and error rates were analyzed across different groups and frame averaging settings.

Main Results:

  • The highest error rate (51.5-57.6%) was observed in the retinochoroidal abnormality (RC) group, significantly higher than other groups (p < 0.05).
  • Error rates in the NF, NP, and R groups ranged from 8.3-16.7%, 15.0-30.0%, and 16.7-33.3%, respectively.
  • Increasing the number of averaged frames did not significantly reduce the error rate in any of the studied groups (p > 0.05).

Conclusions:

  • Automated choroidal segmentation demonstrates a high error rate, particularly in the presence of choroidal abnormalities.
  • Frame averaging techniques do not significantly improve the accuracy of automated choroidal segmentation in these cases.
  • Further advancements in automated segmentation technology are necessary to enhance precision for clinical applications.