MISeval: a Metric Library for Medical Image Segmentation Evaluation
This provides a tool for researchers and practitioners in medical imaging to ensure consistent performance assessment, though it is incremental as it packages existing metrics rather than introducing new ones.
The authors tackled the lack of a universal Python metric library for standardized and reproducible evaluation of medical image segmentation models by proposing MISeval, an open-source package that is publicly available on PyPI and GitHub.
Correct performance assessment is crucial for evaluating modern artificial intelligence algorithms in medicine like deep-learning based medical image segmentation models. However, there is no universal metric library in Python for standardized and reproducible evaluation. Thus, we propose our open-source publicly available Python package MISeval: a metric library for Medical Image Segmentation Evaluation. The implemented metrics can be intuitively used and easily integrated into any performance assessment pipeline. The package utilizes modern CI/CD strategies to ensure functionality and stability. MISeval is available from PyPI (miseval) and GitHub: https://github.com/frankkramer-lab/miseval.