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A reproducible data-driven parameter optimization framework for classical skull stripping methods across

Nila Prasetya Aryani^1,2, Freddy Haryanto^1,2, Siti Nurul Khotimah^1,2

¹Physics Department, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, Indonesia.

Biomedical Physics & Engineering Express

|June 1, 2026

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This study introduces a new framework for brain magnetic resonance imaging (MRI) skull stripping, improving classical methods like Brain Surface Extractor (BSE) for reproducible and accurate results across diverse datasets.

Area of Science:

Neuroimaging
Medical Image Analysis
Computational Neuroscience

Background:

Accurate skull stripping is crucial for brain MRI analysis but classical methods are parameter-sensitive.
Reproducibility across diverse datasets is a challenge for existing skull stripping techniques.

Purpose of the Study:

To develop a statistically reproducible, dataset-level parameter optimization framework for classical skull stripping methods.
To enhance the adaptability and robustness of the Brain Surface Extractor (BSE) across heterogeneous MRI datasets.

Main Methods:

Proposed a novel statistical tuning strategy for BSE, deriving and recombining diffusion and edge parameters based on dataset descriptors.
Evaluated optimized Brain Extraction Tool (BET) and BSE on LPBA40, NFBS, and CC359 datasets, benchmarking against deep learning methods (HDBET, SynthStrip).

Keywords:

brain MRI classical skull stripping parameter optimization

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Assessed performance using Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff distance (HD95) for overlap and boundary accuracy.

Main Results:

Optimized BSE achieved high adaptability across heterogeneous datasets (DSC 0.96), approaching deep learning performance.
High overlap accuracy (DSC) did not always correlate with accurate boundary delineation (HD95).
The proposed framework improved robustness and reduced failure rates compared to random parameter sampling, especially on heterogeneous data.

Conclusions:

Dataset-level parameter optimization enables classical skull stripping methods to achieve competitive performance with enhanced reproducibility and transparency.
Optimized BSE offers superior adaptability and boundary stability on heterogeneous datasets compared to BET, with a favorable computational trade-off.
Classical methods provide a computationally accessible alternative to deep learning, balancing accuracy with hardware independence.