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MISM: A Medical Image Segmentation Metric for Evaluation of Weak Labeled Data
Dennis Hartmann1, Verena Schmid1,2, Philip Meyer2
1IT-Infrastructure for Translational Medical Research, University of Augsburg, 86159 Augsburg, Germany.
Diagnostics (Basel, Switzerland)
|August 26, 2023
Summary
A new medical image segmentation metric, MISm (Medical Image Segmentation metric), effectively addresses limitations of current measures, particularly for edge cases like small or absent regions of interest, ensuring broader applicability.
Area of Science:
- Medical Imaging
- Computer Vision
- Algorithm Evaluation
Background:
- Current performance measures for medical image segmentation algorithms have limitations.
- These limitations are evident when evaluating images with very small or absent regions of interest.
Purpose of the Study:
- To introduce and evaluate a novel medical image segmentation metric, MISm.
- To address the shortcomings of existing metrics in handling edge cases.
Main Methods:
- MISm is proposed as a composite metric combining the Dice similarity coefficient and weighted specificity.
- The metric was analyzed for definition gaps, scoring gradient, and optimal weighting coefficients.
- Evaluation involved comparing MISm with popular metrics using simulated MRI data across various scenarios.
Main Results:
- MISm demonstrates general applicability and effectively handles edge cases not covered by other metrics.
- The proposed metric provides a reasonable assessment in challenging segmentation scenarios.
Conclusions:
- MISm offers a more robust and comprehensive evaluation of medical image segmentation algorithms.
- The inclusion of MISm in the MISeval framework promotes its community adoption and ensures result reproducibility.
