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Deep-DM: Deep-Driven Deformable Model for 3D Image Segmentation Using Limited Data.

Helena R Torres, Bruno Oliveira, Anne Fritze

    IEEE Journal of Biomedical and Health Informatics
    |August 7, 2024
    PubMed
    Summary
    This summary is machine-generated.

    Deep-DM, a novel framework, enhances 3D medical image segmentation using limited data by integrating a learned energy function with deformable models. This approach is less data-dependent than deep learning (DL) methods, improving segmentation accuracy with fewer training samples.

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    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Machine Learning

    Background:

    • Medical image segmentation is crucial for clinical tasks like diagnosis and treatment planning.
    • Deep Learning (DL) methods are state-of-the-art but require large annotated datasets, which are often scarce in clinical practice, especially for 3D images.

    Purpose of the Study:

    • To propose Deep-DM, a learning-guided deformable model framework for 3D medical image segmentation.
    • To address the challenge of limited training data in medical image segmentation.

    Main Methods:

    • A Convolutional Neural Network (CNN) learns an energy function integrated into an explicit deformable model.
    • The energy function is iteratively retrieved from localized anatomical image representations around the evolving surface.
    • This focuses on regions of interest, excluding irrelevant information to aid the learning process.

    Main Results:

    • The Deep-DM framework demonstrated feasibility across diverse 3D medical image segmentation tasks (left ventricle, fetal head, left atrium, bladder) and modalities (ultrasound, MRI, CT).
    • The approach showed reduced dependence on training dataset size compared to state-of-the-art DL methods.
    • Deep-DM outperformed DL methods when limited training samples were available.

    Conclusions:

    • Deep-DM offers a robust and data-efficient approach for segmenting anatomical structures in 3D medical images.
    • The method has the potential to significantly enhance clinical tasks reliant on accurate image segmentation.