A General and Balanced Region-Based Metric for Evaluating Medical Image Segmentation Algorithms

Evaluating medical imaging segmentation is a very complex problem. Several papers proposed methodologies and different metrics pursuing more reliable and unbiased procedures. In this paper, we propose a novel accuracy metric which is more balanced than the well known Dice and Jaccard coefficients. We also prove mathematically that the proposed metric generalizes Dice, Jaccard and the previously proposed Balanced Dice and Balanced Jaccard coefficients. Our experiments show that significant changes in brain tissue segmentation evaluation results are noticed as we applied our new metric.