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<p>Globally, brain tumors are among the most fatal malignancies in humans, and accurate segmentation of their intricate sub-regions remains a challenging and subjective task in clinical practice. This study proposes a cascaded 3D U-Net model with Pre-Activation Residual Squeeze and Excitation Attention to address these challenges. The model leverages ResNet34 and ResNext50 pre-trained encoders with ImageNet weights to enhance feature extraction, followed by a hierarchical segmentation strategy: coarse segmentation (whole tumor) and fine segmentation (tumor core and enhancing tumor). Two novel attention mechanisms: Cascaded Pre-Activation Residual Attention and Pre-Activation Residual SE Attention are introduced to focus the model on smaller tumor sub-regions. Experiments on the BraTS 2018 and 2019 datasets demonstrate the state-of-the-art performance. On BraTS 2018, the model achieves Dice scores of 91.9% (whole tumor), 95.2% (tumor core), and 98% (enhancing tumor), with corresponding IOU scores of 90.8%, 94.5%, and 97.5%. On BraTS 2019, it attains Dice scores of 90.2% (whole tumor), 95% (tumor core), and 95.6% (enhancing tumor), alongside IOU scores of 90%, 94.7%, and 99.6%. These results significantly outperform existing approaches, particularly in segmenting small and heterogeneous tumor sub-regions.</p>

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A cascaded 3D-Unet with pre-activation residual SE attention for accurate brain tumor segmentation method in MRI imaging

  • 期刊论文

  • 发表日期：

    2025年9月2日

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  • Justice Kwame Appati,
  • Richard Baah Nkansah,
  • Leonard Mensah Boante,
  • Stephen Akatore Atimbire,
  • Michael Agbo Tettey Soli

摘要

Globally, brain tumors are among the most fatal malignancies in humans, and accurate segmentation of their intricate sub-regions remains a challenging and subjective task in clinical practice. This study proposes a cascaded 3D U-Net model with Pre-Activation Residual Squeeze and Excitation Attention to address these challenges. The model leverages ResNet34 and ResNext50 pre-trained encoders with ImageNet weights to enhance feature extraction, followed by a hierarchical segmentation strategy: coarse segmentation (whole tumor) and fine segmentation (tumor core and enhancing tumor). Two novel attention mechanisms: Cascaded Pre-Activation Residual Attention and Pre-Activation Residual SE Attention are introduced to focus the model on smaller tumor sub-regions. Experiments on the BraTS 2018 and 2019 datasets demonstrate the state-of-the-art performance. On BraTS 2018, the model achieves Dice scores of 91.9% (whole tumor), 95.2% (tumor core), and 98% (enhancing tumor), with corresponding IOU scores of 90.8%, 94.5%, and 97.5%. On BraTS 2019, it attains Dice scores of 90.2% (whole tumor), 95% (tumor core), and 95.6% (enhancing tumor), alongside IOU scores of 90%, 94.7%, and 99.6%. These results significantly outperform existing approaches, particularly in segmenting small and heterogeneous tumor sub-regions.