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    This study introduces a novel deep learning approach for glaucoma diagnosis using retinal depth estimation and optic nerve head segmentation from fundus images. The method improves accuracy in detecting glaucoma cues, aiding early diagnosis and treatment.

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    Area of Science:

    • Ophthalmology
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Glaucoma diagnosis relies on examining the optic nerve head (ONH) using color fundus images (CFI).
    • Accurate segmentation of the optic disc and optic cup is crucial for calculating the optic cup-to-disc ratio (CDR), a key glaucoma indicator.
    • Assessing depth variation in the ONH region is also vital for monitoring glaucoma progression.

    Purpose of the Study:

    • To develop a deep learning framework for estimating retinal depth from single fundus images.
    • To propose a novel pretraining scheme, pseudo-depth reconstruction, to address labeled data insufficiency in monocular retinal depth estimation.
    • To create a guided network for optic disc and cup segmentation that utilizes estimated depth maps.

    Main Methods:

    • A deep learning framework was developed for monocular retinal depth estimation, employing a novel pseudo-depth reconstruction pretraining scheme.
    • A fully convolutional guided network was proposed for optic disc and cup segmentation, incorporating a multimodal feature extraction block to fuse color and depth information.
    • The methods were evaluated on multiple public datasets: INSPIRE for depth estimation, and ORIGA, RIMONEr3, and DRISHTI-GS for segmentation.

    Main Results:

    • Pseudo-depth reconstruction demonstrated superior performance as a proxy task compared to denoising for retinal depth estimation.
    • The proposed depth estimation method outperformed existing techniques on the INSPIRE dataset.
    • The guided segmentation network achieved performance comparable to, and in many cases superior to, state-of-the-art methods on three different datasets.

    Conclusions:

    • The developed deep learning framework effectively estimates retinal depth and segments optic nerve head structures from color fundus images.
    • The novel pseudo-depth reconstruction pretraining scheme successfully addresses data limitations in monocular depth estimation.
    • The integration of depth information significantly enhances the accuracy of optic disc and cup segmentation, offering a promising tool for glaucoma screening and diagnosis.