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Related Concept Videos

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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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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Related Experiment Video

Updated: Jul 8, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
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An Enhanced Synthetic Cystoscopic Environment for Use in Monocular Depth Estimation.

Peter Somers, Mario Deutschmann, Simon Holdenried-Krafft

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study enhances depth estimation for endoscopic surgery by improving synthetic data generation. The new method improves device positioning accuracy in real-world minimally invasive procedures.

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    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Robotic Surgery

    Background:

    • Accurate device positioning is crucial for minimally invasive surgeries.
    • Current optical localization methods struggle in dynamic, non-rigid surgical environments like cystoscopies.
    • Monocular endoscopic camera images present challenges for precise tracking.

    Purpose of the Study:

    • To develop an improved method for supervised depth estimation using neural networks.
    • To reduce the domain gap between synthetic and real endoscopic images.
    • To enhance the accuracy of device positioning during manual, minimally invasive procedures.

    Main Methods:

    • Utilized neural networks for supervised depth estimation from synthetic images.
    • Employed adversarial training in a second step to apply the network to real images.
    • Enhanced a synthetic cystoscopic environment to minimize the domain gap.

    Main Results:

    • The proposed enhanced synthetic environment demonstrated distinct improvements over previous work.
    • The trained network showed superior performance when applied to real test images.
    • Achieved more accurate device positioning in simulated and real endoscopic scenarios.

    Conclusions:

    • Improved synthetic data generation and adversarial training enhance depth estimation accuracy.
    • The developed method offers a more robust solution for device tracking in minimally invasive surgery.
    • This approach has the potential to improve surgical precision and patient outcomes.