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Skin Surface Detection in 3D Optoacoustic Mesoscopy Based on Dynamic Programming.

Suhanyaa Nitkunanantharajah, Guillaume Zahnd, Malini Olivo

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    A new dynamic programming method accurately detects skin surfaces in 3D optoacoustic images. This automated approach enhances disease diagnosis and management by improving the speed and precision of skin imaging analysis.

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    Area of Science:

    • Biomedical Imaging
    • Medical Technology
    • Computational Imaging

    Background:

    • Optoacoustic (photoacoustic) mesoscopy provides unique skin imaging capabilities for disease detection and management.
    • Accurate skin surface identification is crucial for quantitative analysis of optoacoustic images.
    • Existing surface detection algorithms struggle with the discontinuities and diffuse interfaces common in 3D raster-scan optoacoustic mesoscopy (RSOM) images.

    Purpose of the Study:

    • To develop a novel, automated method for rapid and reliable skin surface detection in 3D RSOM images.
    • To address the limitations of current algorithms in handling complex skin boundary characteristics.
    • To improve the efficiency and accuracy of quantitative analysis in clinical optoacoustic imaging.

    Main Methods:

    • A novel dynamic programming approach was implemented for single-step 2D skin surface extraction.
    • A domain-specific energy function was developed, tailored to the properties of volumetric optoacoustic mesoscopy data.
    • The method was validated on 3D RSOM scans of 19 volunteers with diverse skin complexions, using manually traced skin surfaces as a reference.

    Main Results:

    • The proposed dynamic programming method successfully extracted skin boundaries as a 2D surface in a single step.
    • Validation demonstrated high accuracy on scans from volunteers with varying skin types.
    • The method's robustness was confirmed, even on images with low-contrast or ill-defined skin boundaries.

    Conclusions:

    • The novel dynamic programming approach offers a reliable and automated solution for skin surface detection in 3D RSOM imaging.
    • This method enhances the speed and accuracy of quantitative feature analysis, facilitating clinical translation of RSOM.
    • The approach shows potential for extension to other surface identification tasks within RSOM and different imaging modalities.