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Volumetric Choroidal Segmentation Using Sequential Deep Learning Approach in High Myopia Subjects.

Dheo A Y Cahyo, Damon W K Wong, Ai Ping Yow

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel deep learning method for segmenting the choroid in 3D scans. The approach accurately maps choroidal volume, aiding in the evaluation of ocular diseases.

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    Area of Science:

    • Ophthalmology
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Choroidal changes are linked to various ocular diseases.
    • Accurate choroidal segmentation is essential for studying these changes.
    • Existing segmentation methods primarily use 2D scans, limiting volumetric analysis.

    Purpose of the Study:

    • To develop and evaluate a deep learning model for volumetric choroidal segmentation.
    • To address the gap in reported methods for segmenting the entire choroidal volume.
    • To improve the analysis and monitoring of ocular diseases through automated segmentation.

    Main Methods:

    • A sequential segmentation approach utilizing a U-Net variant with a bidirectional Convolutional Long Short-Term Memory (C-LSTM) module.
    • Model evaluation on volumetric Swept Source Optical Coherence Tomography (SS-OCT) scans from 40 high myopia subjects.
    • Comparison with other U-Net-based segmentation variants.

    Main Results:

    • The proposed model achieved a high Intersection over Union (IoU) accuracy of 0.92 for volumetric choroidal segmentation.
    • Demonstrated superior performance compared to other U-Net-based variants.
    • Successfully segmented choroidal volumes from SS-OCT data.

    Conclusions:

    • The developed deep learning approach enables accurate automatic segmentation of choroidal volume.
    • This method facilitates enhanced evaluation and monitoring of ocular diseases.
    • The findings support the clinical relevance of AI in ophthalmological imaging analysis.