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Purpose: <p>This study proposes DMCIE (diffusion model with concatenation of inputs and errors) to enhance binary brain tumor segmentation from multimodal MRI scans. Accurate voxel-wise tumor localization remains challenging due to variability in tumor size, shape, and imaging conditions, impacting clinical diagnosis and treatment planning.</p> Methods: <p>DMCIE employs a two-stage framework: a 3D U-Net first predicts an initial tumor mask from multimodal MRI inputs (T1, T1ce, T2, FLAIR), and an error map highlighting discrepancies with the ground truth is generated. This error map, concatenated with the original inputs, is refined through a diffusion model that iteratively corrects misclassified and boundary regions.</p> Results: <p>The proposed DMCIE method was evaluated on the BraTS2020 dataset. Compared to the initial U-Net segmentation, DMCIE improved segmentation performance by +5.18% Dice and <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(-\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>-</mo> </math></EquationSource> </InlineEquation>2.07 mm HD95 compared to the initial U-Net segmentation. It shows improvements in boundary accuracy and segmentation across diverse tumor shapes, and maintains spatial coherence, even in fragmented cases.</p> Conclusion: <p>DMCIE introduces an effective error-guided correction mechanism for binary brain tumor segmentation, using multimodal MRI data to enhance segmentation accuracy. By modeling and correcting segmentation errors during diffusion, DMCIE achieves anatomically precise and well-localized tumor segmentation.</p>

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Dmcie: Diffusion model with concatenation of inputs and errors for enhanced brain tumor segmentation in MRI images

  • 期刊论文

  • 发表日期：

    2025年10月23日

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  • Sara Yavari,
  • Rahul Nitin Pandya,
  • Jacob Furst

摘要

Purpose:

This study proposes DMCIE (diffusion model with concatenation of inputs and errors) to enhance binary brain tumor segmentation from multimodal MRI scans. Accurate voxel-wise tumor localization remains challenging due to variability in tumor size, shape, and imaging conditions, impacting clinical diagnosis and treatment planning.

Methods:

DMCIE employs a two-stage framework: a 3D U-Net first predicts an initial tumor mask from multimodal MRI inputs (T1, T1ce, T2, FLAIR), and an error map highlighting discrepancies with the ground truth is generated. This error map, concatenated with the original inputs, is refined through a diffusion model that iteratively corrects misclassified and boundary regions.

Results:

The proposed DMCIE method was evaluated on the BraTS2020 dataset. Compared to the initial U-Net segmentation, DMCIE improved segmentation performance by +5.18% Dice and \(-\) - 2.07 mm HD95 compared to the initial U-Net segmentation. It shows improvements in boundary accuracy and segmentation across diverse tumor shapes, and maintains spatial coherence, even in fragmented cases.

Conclusion:

DMCIE introduces an effective error-guided correction mechanism for binary brain tumor segmentation, using multimodal MRI data to enhance segmentation accuracy. By modeling and correcting segmentation errors during diffusion, DMCIE achieves anatomically precise and well-localized tumor segmentation.