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Depth Perception and Spatial Vision01:15

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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A Novel Dual-Modal Deep Learning Approach for Real-Time Removal of Hepatic Fluorescence in Indocyanine Green-Guided Laparoscopic Cholecystectomy
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Coupled sparse dictionary for depth-based cup segmentation from single color fundus image.

Arunava Chakravarty, Jayanthi Sivaswamy

    Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
    |October 22, 2014
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    Summary
    This summary is machine-generated.

    This study introduces a new method for extracting optic cup boundaries from fundus images using depth estimation. The framework accurately segments the optic cup, outperforming existing techniques.

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    Area of Science:

    • Ophthalmology
    • Medical Imaging
    • Computer Vision

    Background:

    • Accurate optic cup segmentation is crucial for diagnosing glaucoma.
    • Current methods often require multiple images or specialized equipment.
    • A non-invasive, single-image approach is highly desirable.

    Purpose of the Study:

    • To develop a novel framework for optic cup boundary extraction using depth estimation from single 2D color fundus photographs.
    • To correlate image-derived depth estimates with Optical Coherence Tomography (OCT) data.
    • To evaluate the performance of the proposed segmentation method against state-of-the-art techniques and expert markings.

    Main Methods:

    • A coupled sparse dictionary approach trained on image-depth pairs to estimate depth from shading, color, and texture gradients.
    • Formulation of a Markov Random Field on the depth map to model cup boundary discontinuities.
    • Leave-one-out validation for depth estimation and evaluation on multiple datasets (INSPIRE, DRISHTI-GS) and comparison with OCT ground truth.

    Main Results:

    • Depth estimation achieved an average correlation coefficient of 0.80 on the INSPIRE dataset.
    • Optic cup segmentation outperformed state-of-the-art methods on the DRISHTI-GS dataset with an average F-score of 0.81 and boundary error of 21.21 pixels.
    • Evaluation against OCT ground truth showed low average RMS errors of 0.11 for Cup-Disk diameter and 0.19 for Cup-Disk area ratios.

    Conclusions:

    • The proposed framework enables accurate optic cup boundary extraction from single 2D fundus images.
    • The method demonstrates superior performance compared to existing techniques and provides reliable depth estimation.
    • This approach holds potential for improved, non-invasive glaucoma assessment.