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A Self-Supervised Network-Based Smoke Removal and Depth Estimation for Monocular Endoscopic Videos.

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    This study introduces a novel self-supervised method for accurate depth estimation in laparoscopic surgery videos, effectively removing smoke and preserving surgical site details without manual labels or CT scans.

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    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Surgical Technology

    Background:

    • Accurate depth estimation in monocular laparoscopic videos is crucial for minimally invasive surgery but is hindered by smoke.
    • Existing methods often require manual labels or patient-specific data, limiting their applicability.

    Purpose of the Study:

    • To develop a label-free, self-supervised method for monocular endoscopic depth estimation that effectively removes smoke.
    • To improve the accuracy and real-time performance of depth estimation in challenging surgical environments.

    Main Methods:

    • A two-step approach: de-endoscopic smoke removal using a cyclic GAN (DS-cGAN) with a generator network (SGEM, RDBM, RUCM), followed by depth estimation using a high-resolution residual U-Net (HRR-UNet).
    • The HRR-UNet incorporates a DepthNet and two PoseNets, utilizing adjacent frames for camera self-motion estimation.
    • The method is self-supervised, requiring no manual labeling or CT scans.

    Main Results:

    • The proposed method effectively removes smoke while preserving critical surgical site details like blood vessels, contours, and textures.
    • Achieved accurate depth information in real surgical scenes, outperforming state-of-the-art methods.
    • Demonstrated real-time performance with a frame rate of 94.45fps.

    Conclusions:

    • The developed self-supervised, smoke-removal depth estimation method offers a promising solution for enhancing surgical navigation in minimally invasive procedures.
    • Its label-free nature and real-time capability make it suitable for clinical applications.