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
This review provides critical evidence that prioritizing advanced AI architectures is key for accurate brain tumor segmentation in MRI, advancing radiology workflows and research focus. Understanding the limitations will help guide development of more deployable and generalizable models in clinical practice.
Source
EurekAlert
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