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nnUNet-based Multi-modality Breast MRI Segmentation and Tissue-Delineating Phantom for Robotic Tumor Surgery

Motaz Alqaoud, John Plemmons, Eric Feliberti

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
    PubMed
    Summary

    This study presents a novel deep learning approach for segmenting breast MRI, achieving high accuracy for fatty, fibroglandular, and tumor tissues. A new breast phantom supports planning for image-guided robotic surgery.

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    Area of Science:

    • Medical imaging analysis
    • Artificial intelligence in surgery
    • Biomedical engineering

    Background:

    • Accurate segmentation of breast tissues in MRI is vital for diagnosing breast masses.
    • Existing methods require robust algorithmic and phantomic foundations for surgical planning.

    Purpose of the Study:

    • To develop an advanced medical image segmentation architecture for breast MRI.
    • To propose a novel polyvinyl alcohol cryogel (PVA-C) breast phantom for surgical planning and navigation.

    Main Methods:

    • Utilized a dual neural network architecture based on nnU-Net for segmenting breast MRI data.
    • Employed a two-stage labeling process: single-class for breast region, then three-class for fatty, fibroglandular (FGT), and tumorous tissues.
    • Developed an automated segmentation approach for the PVA-C breast phantom.

    Main Results:

    • Achieved a Dice Similarity Coefficient (DSC) of 0.95 for breast region segmentation.
    • The second network demonstrated DSCs of 0.95 (fat), 0.83 (FGT), and 0.41 (tumor).
    • The PVA-C phantom facilitates planning and navigation experiments for robotic breast surgery.

    Conclusions:

    • The developed deep learning algorithm and breast phantom provide a foundation for image-guided robotic breast surgery.
    • This approach integrates preoperative MRI and intraoperative ultrasound for enhanced surgical planning and navigation.
    • The technology aims to improve surgical accuracy, patient outcomes, and cosmetic results in breast cancer treatment.