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Patch-Based Cervical Cancer Segmentation using Distance from Boundary of Tissue.

Kengo Araki, Mariyo Rokutan-Kurata, Kazuhiro Terada

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
    PubMed
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    Automating cancer diagnosis is crucial. This study introduces a new method using tissue boundary distance to improve whole slide image analysis for cervical cancer classification, enhancing accuracy over traditional approaches.

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    Area of Science:

    • Digital pathology
    • Computational oncology
    • Medical image analysis

    Background:

    • Automated pathological diagnosis is essential for detailed cancer examination.
    • Whole Slide Images (WSIs) require patch-based segmentation due to their large size.
    • Patch-based methods often omit crucial global information for accurate classification.

    Purpose of the Study:

    • To develop an automated method for cancer area segmentation and classification.
    • To incorporate global image information into patch-based segmentation for improved accuracy.
    • To evaluate the efficacy of a novel approach using Distance from the Boundary of tissue (DfB) for cervical cancer classification.

    Main Methods:

    • Utilized Distance from the Boundary of tissue (DfB) as a source of global information.
    • Applied a patch-based approach for segmentation of Whole Slide Images (WSIs).
    • Experimentally validated the method on a three-class classification task for cervical cancer.

    Main Results:

    • The proposed method successfully incorporated global information (DfB) into the analysis.
    • The DfB-enhanced method demonstrated improved performance in cervical cancer classification.
    • Performance gains were observed compared to conventional patch-based methods without global context.

    Conclusions:

    • Integrating global information, such as DfB, enhances automated pathological diagnosis.
    • The DfB method offers a promising approach for improving the accuracy of WSI analysis.
    • This technique has the potential to advance automated cervical cancer classification and pathological diagnosis.