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Self-assessment for optic disc segmentation.

Jun Cheng, Jiang Liu, Fengshou Yin

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 11, 2013
    PubMed
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    Combining results from multiple optic disc segmentation methods improves accuracy and reduces failures in retinal image analysis. This approach enhances automated glaucoma diagnosis by providing a more reliable segmentation outcome.

    Area of Science:

    • Ophthalmology
    • Medical Image Analysis
    • Computer Vision

    Background:

    • Accurate optic disc segmentation in retinal fundus images is crucial for automated glaucoma diagnosis.
    • Existing segmentation methods often exhibit variability and fail in specific scenarios, limiting their clinical utility.

    Purpose of the Study:

    • To develop a robust optic disc segmentation method by combining results from multiple individual techniques.
    • To enhance the reliability and reduce failure cases in automated optic disc segmentation.

    Main Methods:

    • Computation of confidence scores for three distinct optic disc segmentation methods.
    • Integration of segmentation results based on their confidence scores to achieve an optimal outcome.

    Main Results:

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    • The combined segmentation approach significantly outperformed individual methods.
    • A relative reduction of 7.4% in mean overlapping error was achieved compared to the best individual method.
    • A substantial decrease in the number of failed segmentation cases with large overlapping errors was observed.

    Conclusions:

    • Combining multiple optic disc segmentation methods offers superior performance over single methods.
    • This ensemble approach enhances the reliability of automated optic disc segmentation, facilitating clinical deployment.