
A systematic review highlights that AI model architecture, more than data or imaging specifics, drives improved tumor segmentation in brain MRI scans.
Key Details
- 1Researchers from the University of Auckland and affiliated institutes reviewed 34 studies on AI-based meningioma segmentation using MRI (2020-2025).
- 2Advanced AI architectures, such as DeepLabV3+, achieved the highest Dice scores (up to 0.98), indicating superior delineation performance.
- 3Model performance tiers were identified: Tier 1 (Dice 0.9-1, hybrid/advanced models), Tier 2 (Dice 0.8-0.9, e.g. 2D U-Net), Tier 3 (Dice <0.82, lightweight models).
- 4Data and imaging modality improvements contributed less to performance than advancements in AI architectures.
- 5Challenges remain, including underperformance for small tumors (<3 mL), high computational demands, and limited generalizability across hospitals.
- 6Most studies used contrast-enhanced T1 MRI; real-world deployment still faces workflow and infrastructure hurdles.
Why It Matters

Source
EurekAlert
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