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Macular hole morphology and measurement using an automated three-dimensional image segmentation algorithm.

Yunzi Chen1, Amar V Nasrulloh2, Ian Wilson3

  • 1Sunderland Eye Infirmary, Sunderland, UK.

BMJ Open Ophthalmology
|August 27, 2020
PubMed
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Three-dimensional automated analysis of macular holes (MH) reveals significant asymmetry, offering more accurate and repeatable measurements than traditional human assessment. This advanced imaging technique improves understanding of MH dimensions for better treatment guidance.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computational Biology

Background:

  • Macular holes (MH) are classified by size, a key predictor of surgical outcomes.
  • Accurate MH dimension measurement is crucial for predicting anatomical and visual success.
  • Current 2D measurements may not fully capture the complex morphology of MH.

Purpose of the Study:

  • To compare the accuracy and repeatability of 3D automated OCT analysis with 2D clinician measurements of MH.
  • To investigate the dimensional asymmetry of MH using a novel 3D algorithm.
  • To assess the impact of 3D analysis on MH classification.

Main Methods:

  • 104 macular hole (MH) cases analyzed using high-density OCT.
  • Automated 3D segmentation algorithm used to derive MH volume, surface area, and dimensions.
Keywords:
anatomydiagnostic tests/investigationimagingmacularetina

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  • Comparison of 3D algorithm-derived parameters with 2D measurements by two independent clinicians.

    • Main Results:

    • MH exhibit significant asymmetry in all dimensions, with minima typically 90° to the horizontal.
    • 3D analysis revealed differences up to 55 μm in MH dimensions compared to 2D measurements.
    • Significant interobserver variability noted in human measurements, leading to reclassification in 25% of cases.

    Conclusions:

    • Macular holes are complex, asymmetric structures.
    • 3D automated OCT analysis provides more accurate and repeatable MH dimension assessment than manual methods.
    • This advanced analysis can enhance future studies on MH progression and treatment outcomes.