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Segmentation of Arm Ultrasound Images in Breast Cancer-Related Lymphedema: A Database and Deep Learning Algorithm.

Sobhan Goudarzi, Jesse Whyte, Mathieu Boily

    IEEE Transactions on Bio-Medical Engineering
    |April 7, 2023
    PubMed
    Summary

    Breast Cancer-Related Lymphedema (BCRL) diagnosis is improved by new automatic ultrasound tissue segmentation methods. A novel dataset and Gated Shape Convolutional Neural Network (GSCNN) enhance BCRL staging and monitoring.

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    Area of Science:

    • Medical imaging
    • Biomedical engineering
    • Machine learning for healthcare

    Background:

    • Breast cancer treatment can lead to Breast Cancer-Related Lymphedema (BCRL), characterized by excess arm volume.
    • Ultrasound imaging is a safe and portable tool for BCRL diagnosis and monitoring.
    • Key biomarkers for BCRL in ultrasound include skin, subcutaneous fat, and muscle thickness.

    Purpose of the Study:

    • To develop and validate an automated method for segmenting tissue layers in ultrasound images for BCRL assessment.
    • To introduce a novel, publicly available ultrasound dataset for BCRL research.
    • To improve the accuracy and reproducibility of BCRL staging and monitoring.

    Main Methods:

    • A publicly available dataset of Radio-Frequency (RF) ultrasound data from 39 subjects was created, including expert manual segmentation masks.
    • A Gated Shape Convolutional Neural Network (GSCNN) was modified for precise automatic segmentation of tissue layers.
    • The CutMix augmentation strategy was employed to enhance the generalization performance of the GSCNN model.

    Main Results:

    • High inter- and intra-observer reproducibility of manual segmentation masks (DSC of 0.94±0.08 and 0.92±0.06, respectively).
    • The modified GSCNN achieved an average Dice Score Coefficient (DSC) of 0.87±0.11 on the test set, demonstrating high performance.
    • The developed method shows effectiveness in segmenting critical tissue layers for BCRL analysis.

    Conclusions:

    • Automated segmentation of ultrasound images can enable convenient and accessible staging of BCRL.
    • The provided dataset and developed methods can significantly facilitate the advancement and validation of BCRL diagnostic tools.
    • Accurate and timely BCRL diagnosis is essential for preventing irreversible patient damage.