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Related Concept Videos

Endoscopic Procedures III: Video Capsule Endoscopy01:28

Endoscopic Procedures III: Video Capsule Endoscopy

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Capsule endoscopy, or wireless or video capsule endoscopy, is a diagnostic procedure for examining the entire gastrointestinal tract. Patients swallow a capsule about the size of a vitamin tablet. The capsule is equipped with a transmitter, a battery, an LED light source, and a color video camera to capture images throughout the gastrointestinal tract. This procedure is particularly useful for diagnosing conditions such as Crohn's disease, ulcerative colitis, tumors, polyps, ulcers,...
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Endoscopic Adaptive Transformer for Enhanced Polyp Segmentation in Endoscopic Imaging.

Yan Pang, Yucheng Long, Zibin Chen

    IEEE Transactions on Medical Imaging
    |September 29, 2025
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    The Endoscopic Adaptive Transformer (EAT) framework accurately segments colorectal polyps in endoscopic images, improving early cancer detection. Its adaptive perception module enhances precision for diverse polyp shapes and sizes.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Gastroenterology

    Background:

    • Polyp segmentation in endoscopic imaging is crucial for colorectal cancer (CRC) early detection.
    • Morphological variability and indistinct boundaries of polyps complicate segmentation.
    • Conventional methods lack adaptability to diverse polyp structures.

    Purpose of the Study:

    • To introduce a novel framework, the Endoscopic Adaptive Transformer (EAT), for enhanced polyp segmentation.
    • To address the limitations of conventional methods in handling complex polyp morphologies.
    • To improve diagnostic accuracy in clinical endoscopy.

    Main Methods:

    • Developed the Endoscopic Adaptive Transformer (EAT) framework.
    • Incorporated an adaptive perception module (APM) with an adaptive perceptive-field mechanism.
    • Evaluated performance on single- and multi-target polyp segmentation scenarios.

    Main Results:

    • EAT achieved a 97.77% Dice coefficient and 4.50mm HD95 in single-target segmentation.
    • EAT achieved an 88.02% Dice coefficient and 53.75mm HD95 in multi-target segmentation.
    • Significantly outperformed state-of-the-art methods in both segmentation scenarios.

    Conclusions:

    • EAT demonstrates superior performance in polyp segmentation accuracy.
    • The adaptive perception module effectively captures local details and contextual information.
    • EAT has the potential to advance diagnostic precision and treatment planning in endoscopy.