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MBUNeXt: Multibranch Encoder Aggregation Network Based on Layer-Fusion Strategy for Multimodal Brain Tumor

Qinghao Liu, Yuehao Zhu, Min Liu

    IEEE Transactions on Neural Networks and Learning Systems
    |August 4, 2025
    PubMed
    Summary

    This study introduces a new deep learning network, MBUNeXt, for accurate multimodal brain tumor segmentation (BraTS). The method enhances fusion of multimodal data, improving tumor subregion identification and surgical planning.

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    Area of Science:

    • Medical imaging analysis
    • Artificial intelligence in neurosurgery
    • Computational neuroscience

    Background:

    • Multimodal brain tumor segmentation (BraTS) is crucial for neurosurgery, but faces challenges due to inter-class differences and information redundancy in brain scans.
    • Existing methods struggle with effective fusion of multimodal information, impacting the accuracy of BraTS.

    Purpose of the Study:

    • To develop an advanced deep learning network for precise multimodal brain tumor segmentation.
    • To address limitations in multimodal information fusion and inter-class differences in brain tumor subregions.

    Main Methods:

    • Proposed a novel multibranch UNeXt (MBUNeXt) network featuring a multibranch encoder aggregation (MEA) strategy.
    • Incorporated a multimodal feature attention (MFA) module to filter redundant information and preserve intermodality similarity.
    • Utilized a large-kernel convolution skip (LCS)-connection module to handle features at different scales and address inter-class differences.

    Main Results:

    • Achieved state-of-the-art (SOTA) performance on BraTS2019 and BraTS2021 datasets with average Dice scores of 85.84% and 91.11%, respectively.
    • Demonstrated robust performance on the BraTS-Africa2024 dataset, even with low imaging quality.
    • The proposed MBUNeXt network effectively integrates multimodal information for improved segmentation accuracy.

    Conclusions:

    • The MBUNeXt network significantly advances multimodal brain tumor segmentation accuracy.
    • The method offers a robust and effective solution for precise surgical interventions by improving tumor delineation.
    • The developed approach shows promise for enhancing neurosurgical planning and patient outcomes.