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Related Experiment Video

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Three-Dimensional Shape Modeling and Analysis of Brain Structures
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A Deep Learning Framework for Skull Stripping in Brain MRI.

Mehnaz Tabassum, Abdulla Al Suman, Carlo Russo

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    Summary
    This summary is machine-generated.

    A new deep learning framework using nnUNet significantly improves automated skull-stripping for brain MRI, outperforming existing methods on both normal and tumor-affected brains. This advancement aids in more accurate brain segmentation and analysis, especially for complex cases.

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    Area of Science:

    • Neuroimage computing
    • Medical image analysis
    • Artificial intelligence in radiology

    Background:

    • Skull-stripping is crucial for brain image analysis but current methods struggle with significant morphological changes, like those from brain tumors.
    • Manual segmentation is time-consuming and requires specialized expertise.
    • Existing automated algorithms often fail when tumors are near the skull border, leading to inaccurate brain tissue removal.

    Purpose of the Study:

    • To develop and evaluate a novel deep learning framework for robust skull-stripping in brain MRI.
    • To address the limitations of current skull-stripping techniques in cases of significant brain abnormalities, particularly tumors.
    • To compare the proposed method against established skull-stripping algorithms.

    Main Methods:

    • A novel deep learning framework based on the nnUNet architecture was developed for automated skull-stripping.
    • The method was evaluated on two publicly available datasets: The Neurofeedback Skull-stripped Repository (NFBS) for normal brains and The Cancer Genome Atlas (TCGA) for brain tumor MRIs.
    • Performance was benchmarked against six other leading skull-stripping methods (BSE, ROBEX, UNet, SC-UNet, MV-UNet, 3D U-Net).

    Main Results:

    • The proposed nnUNet-based method demonstrated superior performance compared to the six other evaluated methods.
    • On the NFBS dataset, the method achieved high accuracy with a Dice coefficient of 0.9960, sensitivity of 0.9999, and specificity of 0.9996.
    • On the TCGA brain tumor dataset, it yielded a Dice coefficient of 0.9296, sensitivity of 0.9288, specificity of 0.9866, and accuracy of 0.9762.

    Conclusions:

    • The novel deep learning framework effectively performs skull-stripping, even in the presence of significant brain abnormalities like tumors.
    • This approach offers a more reliable and accurate alternative to existing methods for pre-processing brain MRIs.
    • The findings suggest a significant advancement in automated neuroimage analysis, particularly for clinical applications involving brain tumors.