## 7 Discussion
### Metrics Used to Evaluate Segmentation and Quantification  
The papers mentioned in this review use several evaluation methods and metrics to present their segmentation results including (among others): pixel-level accuracy, sensitivity, and specificity; the Sorensen-Dice coefficient (Dice coefficient, DSC); the Jaccard index (Intersection over Union, IoU); and the area under the receiver operating characteristic curve (AUC). However, some of these metrics are better than others at showing segmentation performance. As an example, consider the pixel-level accuracy. The pixel-level accuracy measurement is usually defined as in Equation (1). \[\text{accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
where \(TP\), \(TN\), \(FP\), and \(FN\) are the number of true positive, true negative, false positive, and false negative pixels, respectively. In the case of EAT or PAT segmentation, the number of positive pixels is usually much smaller than the number of negative (background) pixels, as the tissues themselves are thin and dispersed around the image. This means that a segmented mask that is fully negative (all pixels are 0) would still have high accuracy, as the number of true negatives would be much larger than the number of false positives. It is clear that in cases where the number of positive and negative pixels is highly imbalanced, pixel-level accuracy can be misleading. Instead of using pixel-level accuracy, pixel-level sensitivity, the Dice coefficient, and the Jaccard index are metrics that are much better at representing true segmentation performance in these unbalanced examples. We strongly suggest to future authors of papers in this field to consider these metrics over the pixel-level accuracy. \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Reference** & **Automatic** & **Dataset** & **Segm. Time** & **Result** \\ \hline \multirow{6}{*}{**Sorensen-Dice**} & [PERSON] et al. [49] & fully & CT; 20 patients 878 slices [20] & - & DSC = 0.78 \\ \cline{2-5}  & [PERSON] et al. [49] & fully & CT; 20 patients 878 slices [20] & - & DSC = 0.86 \\ \cline{2-5}  & [PERSON] et al. [37] & fully & 40 patients & - & DSC = 0.85 \(\pm\) 0.05 \\ \cline{2-5}  & [PERSON] et al. [36] & fully & CT; 20 patients 878 slices [20] & - & DSC = 0.91 \(\pm\) 0.01 \(r\) = 0.93 \\ \cline{2-5}  & [PERSON] et al. [38] & fully & CT; 20 patients 878 slices [20] & - & AUC = 0.87 \\ \cline{2-5}  & [PERSON] et al. [7] & fully & CT; 250 patients \(\sim\)13,750 slices & 3–20 s & DSC = 0.82 IQR 0.78–0.86 \(r\) = 0.97 \\ \hline \multirow{6}{*}{**Sorensen-Dice**} & [PERSON] et al. [46] & semi & CT; 20 patients 878 slices [20] & - & DSC = 0.94 \\ \cline{2-5}  & [PERSON] and [PERSON] [47] & fully & CTA; 30 patients & 52 s & DSC = 0.90 \(r\) = 0.98 \\ \cline{2-5}  & [PERSON] et al. [48] & semi & CT; 10 patients & - & DSC = 0.69 \(\pm\) 0.08 \\ \cline{2-5}  & [PERSON] et al. [26] & fully & CT; 30 patients & 51.9 s & DSC = 0.91 \(\pm\) 0.04 \(r\) = 0.99 \\ \cline{2-5}  & [PERSON] et al. [56] & semi & CT; 70 patients \(\sim\)31,500 slices & 133 s & \(\rho\) = 0.97 \\ \cline{2-5}  & [PERSON] et al. [20] & fully & CT; 20 patients 878 slices [20] & 1.8 h & DSC = 0.97 \\ \cline{2-5}  & [PERSON] et al. [45] & fully & CT; 20 patients 878 slices [20] & 24 h & DSC = 0.98 \\ \hline \multirow{6}{*}{**[PERSON]**} & [PERSON] et al. [9] & semi & CT; 145 patients \(\sim\)24,150 slices & 3.5–14.5 min & DSC = 0.93 \(r\) = 0.95 \\ \cline{2-5}  & [PERSON] et al. [24] & fully & CT; 50 patients & 60 s & DSC = 0.92 (0.88–0.95) \(r\) = 0.97 \\ \cline{2-5}  & [PERSON] et al. [42] & fully & CT; 15 patients \(\sim\)825 slices & 60 s & DSC = 0.93 (0.88–0.95) \(r\) = 0.96 \\ \cline{2-5}  & [PERSON] et al. [25] & fully & CT; 98 patients \(\sim\)6272 slices & - & DSC = 0.89 \(\pm\) 0.03 \(r\) = 0.91 \\ \cline{2-5}  & [PERSON] et al. [23] & semi & CT; 10 patients & - & - \\ \cline{2-5}  & [PERSON] [41] & semi & CT; 10 patients & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Epicardial adipose tissue segmentation methods.