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Updated: Dec 6, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.2K

Efficient Deep Learning-based Wound-bed Segmentation For Mobile Applications.

Ee Ping Ong, Christina Tang Ka Yin, Beng-Hai Lee

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
    Summary
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    This study introduces an efficient deep learning model for wound-bed segmentation, outperforming U-Net in speed and accuracy. Its small size allows real-time deployment on mobile devices.

    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Accurate segmentation of wound-bed regions is crucial for effective wound assessment and treatment.
    • Existing deep learning models like U-Net can be computationally intensive and large, limiting their use in resource-constrained environments.

    Purpose of the Study:

    • To develop a novel, efficient deep learning image segmentation network for distinguishing wound-bed regions from the background.
    • To create a model with a significantly smaller parameter count and file size compared to U-Net.
    • To achieve real-time performance on portable devices.

    Main Methods:

    • A novel convolutional neural networks (CNN)-based segmentation network was designed and implemented.
    • The proposed network's architecture was optimized for efficiency and reduced parameter count.

    Related Experiment Videos

    Last Updated: Dec 6, 2025

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.2K
  • Performance was evaluated against U-Net using pixel accuracy and intersection-over-union metrics.

    • Main Results:

    • The proposed CNN model has only 18.1% of the parameters of U-Net, resulting in a smaller model file size.
    • Training time for the proposed model was 40.2% of that required for U-Net.
    • The model demonstrated superior performance in pixel accuracy and intersection-over-union compared to U-Net.

    Conclusions:

    • The developed efficient CNN segmentation model offers a faster and more accurate alternative to U-Net for wound-bed segmentation.
    • The model's small footprint enables real-time deployment on mobile and portable devices, facilitating clinical applications.
    • This research contributes to advancing AI-driven medical image analysis for wound care.