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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

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AUDIT: An open-source Python library for AI model evaluation with use cases in MRI brain tumor segmentation.

Carlos Aumente-Maestro¹, Michael Müller², Beatriz Remeseiro³

¹Artificial Intelligence Center, Universidad de Oviedo, Gijón, Spain; ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.

Computer Methods and Programs in Biomedicine

|August 12, 2025

Summary

This summary is machine-generated.

This study introduces AUDIT, an open-source Python library for evaluating artificial intelligence (AI) segmentation models in medical imaging. AUDIT enhances model generalization and robustness by providing region-specific features and interactive analysis tools.

Keywords:

Brain tumor segmentation Deep learning Medical image analysis Model evaluation

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Area of Science:

Medical Image Analysis
Artificial Intelligence
Machine Learning

Background:

Challenges in AI for medical image analysis include poor model generalization, extensive data requirements, and lack of clinical robustness.
Current evaluation frameworks lack subject-based insights, hindering bias identification and domain shift adaptation.

Purpose of the Study:

Introduce AUDIT, an open-source Python library to improve AI segmentation model evaluation and MRI dataset analysis.
Address limitations in current AI model evaluation for medical imaging.

Main Methods:

AUDIT offers modules for region-specific feature extraction and performance metric calculation.
Includes a dynamic web application for interactive model evaluation and data exploration.
Designed to enhance the analysis of MRI datasets.

Main Results:

AUDIT provides open-source code, tutorials, and documentation for easy installation and use.
Demonstrates versatility and broad applicability through common AI-driven brain tumor segmentation use cases.
Facilitates interactive exploration and evaluation of segmentation models.

Conclusions:

AUDIT fills critical gaps in evaluating AI segmentation models, advancing the field of AI in medical image analysis.
The library supports integration with external tools and applications for broader utility.