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CMHF-3DNet: A Transformer-Based Framework for Improved Brain Tumor Segmentation Across Modalities.

Abid Hussain¹, Wu Jungshen¹, Ali Turab¹

¹School of Software, Northwestern Polytechnical University, Xi'an 710000, China (A.H., W.J., A.T., Y.G., I.A., A.W.).

Academic Radiology

|June 1, 2026

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We developed a novel 3D deep learning model, CMHF-3DNet, for precise brain tumor segmentation in multi-modal MRI. This method improves delineation of critical subregions like the enhancing tumor and tumor core, aiding automated tumor assessment.

Area of Science:

Medical Imaging
Artificial Intelligence
Neuro-oncology

Background:

Accurate brain tumor subregion segmentation in multi-modal MRI is crucial for clinical applications.
Challenges include weak boundaries and appearance variability in enhancing tumor (ET) and tumor core segmentation.

Purpose of the Study:

To introduce Cross-Modal Hierarchical Fusion U-Net 3D (CMHF-3DNet) for improved brain tumor segmentation.
To address challenges in delineating heterogeneous tumor subregions.

Main Methods:

A 3D encoder-decoder framework utilizing voxel-wise cross-modal transformer fusion.
Hierarchy-aware multi-task learning to enforce anatomical consistency (ET ⊂ TC ⊂ WT).

Main Results:

Keywords:

3D U-Net BraTS benchmark Brain tumor segmentation Cross-modal transformer Hierarchy aware learning Medical image segmentation Multi-modal MRI

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Evaluated on BraTS 2023, 2024, and 2025 validation datasets.

Achieved a mean Dice Similarity Coefficient (DSC) of 0.8527 on BraTS 2025 and 0.8526 on BraTS 2023.

Obtained a mean 95th percentile Hausdorff Distance (HD95) of 6.47 mm on BraTS 2025.

Conclusions:

CMHF-3DNet demonstrates improved boundary delineation for tumor subregions, especially ET and tumor core.
The model shows robust performance across multiple BraTS benchmarks.
Suggests potential for automated brain tumor assessment.