MISeval: a Metric Library for Medical Image Segmentation Evaluation

• URL: http://arxiv.org/abs/2201.09395v1
• Date: Sun, 23 Jan 2022 23:06:47 GMT
• Title: MISeval: a Metric Library for Medical Image Segmentation Evaluation
• Authors: Dominik M\"uller, Dennis Hartmann, Philip Meyer, Florian Auer, I\~naki Soto-Rey and Frank Kramer
• Abstract summary: There is no universal metric library in Python for standardized and reproducible evaluation. We propose our open-source publicly available Python package MISeval: a metric library for Medical Image Evaluation.
• Score: 1.4680035572775534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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