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MISeval: A Metric Library for Medical Image Segmentation Evaluation
Dominik Müller1,2, Dennis Hartmann1, Philip Meyer1,2
1IT-Infrastructure for Translational Medical Research, University of Augsburg, Germany.
Studies in Health Technology and Informatics
|May 25, 2022
Summary
Evaluating AI in medicine requires standardized metrics. We introduce MISeval, an open-source Python package for reproducible medical image segmentation evaluation, ensuring reliable AI performance assessment.
Area of Science:
- Artificial Intelligence in Medicine
- Medical Image Analysis
- Software Development for Scientific Research
Background:
- Accurate performance assessment is vital for modern medical AI, particularly deep learning models for image segmentation.
- A standardized, universal Python metric library for reproducible evaluation is currently lacking.
Purpose of the Study:
- To introduce MISeval, an open-source Python package designed for the evaluation of medical image segmentation.
- To provide a standardized and reproducible metric library for AI in medicine.
Main Methods:
- Development of an open-source Python package named MISeval.
- Implementation of various metrics for medical image segmentation evaluation.
- Utilization of modern DevOps strategies for package functionality and stability.
Main Results:
- MISeval offers an intuitive and easily integrable library for performance assessment.
- The package ensures functionality and stability through modern DevOps practices.
- MISeval is publicly available on PyPI and GitHub.
Conclusions:
- MISeval addresses the need for a standardized metric library in medical image segmentation.
- The package facilitates reproducible and reliable evaluation of AI algorithms in medicine.
- MISeval promotes best practices in software development for scientific tools.
