A Guide and Mini-Review on the Performance Evaluation Metrics in Binary Segmentation of Magnetic Resonance Images

Eight previously proposed segmentation evaluation metrics for brain magnetic resonance images (MRI), which are sensitivity (SE), specificity (SP), false-positive rate (FPR), false-negative rate (FNR), positive predicted value (PPV), accuracy (ACC), Jaccard index (JAC) and dice score (DSC) are presented and discussed in this paper. These evaluation metrics could be classified into two groups namely pixel-wise metrics and area-wise metrics. We, also, distill the most prominent previously published papers on brain MRI segmentation evaluation metrics between 2021 and 2023 in a detailed literature matrix. The identification of illness or tumor areas using brain MRI image segmentation is a large area of research. However, there is no single segmentation evaluation metric when evaluating the results of brain MRI segmentation in the current literature. Also, the pixel-wise metrics should be supported with the area-wise metrics such as DSC while evaluating the image segmentation results and each metric should be compared with other metrics for better evaluation.