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Two Path Gland Segmentation Algorithm of Colon Pathological Image Based on Local Semantic Guidance.

Songtao Ding, Hongyu Wang, Hu Lu

    IEEE Journal of Biomedical and Health Informatics
    |September 20, 2022
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    This study introduces a novel two-path algorithm for segmenting cancerous glands in colon pathology images. The method improves the accuracy of detecting colonic adenocarcinoma by enhancing feature extraction and network learning capabilities.

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    Area of Science:

    • Pathology
    • Medical Imaging
    • Computer Vision

    Background:

    • Colonic adenocarcinoma, a life-threatening cancer, originates from mucosal epidermal carcinogenesis.
    • Accurate segmentation of cancerous glands in colon pathology images is crucial for diagnosis.
    • Existing single-network models struggle to precisely segment the transition from benign to malignant glands.

    Purpose of the Study:

    • To develop an advanced two-path gland segmentation algorithm for colon pathology images.
    • To enhance the accuracy of detecting and diagnosing colonic adenocarcinoma.
    • To improve the network's ability to learn morphological features of glands.

    Main Methods:

    • A two-path gland segmentation algorithm utilizing local semantic guidance.
    • An improved candidate region search algorithm for dataset expansion and feature-sensitive sub-datasets.
    • A semantic feature-guided model for local adenocarcinoma feature extraction, combined with an attention-based backbone network for context features.

    Main Results:

    • The algorithm achieved a larger receptive field and richer local feature information.
    • Enhanced network learning capability for gland morphological features.
    • Improved performance in automatic gland segmentation, validated on the Warwick Qu-Dataset.

    Conclusions:

    • The proposed algorithm demonstrates superior performance in Dice coefficient, F1 score, and Hausdorff distance compared to existing methods.
    • The local semantic guidance approach effectively enhances gland segmentation accuracy in colon pathology.
    • This advancement contributes to more precise detection and diagnosis of colonic adenocarcinoma.