Table 4.

The 12 metrics for evaluating the performance of various segmentation models.

Metric	Formula	Description
Receiver operating characteristic (ROC) curve	$TPR=\frac{TP}{TP+FN},FPR=\frac{FP}{FP+TN}$	TPR and FPR measure the proportion of correctly identified actual positives and actual negatives, respectively
Precision-recall (PR) curve	$P=\frac{TP}{TP+FP},R=\frac{TP}{TP+FN}$	PR curve mainly evaluates the comprehensiveness of the detected lung infection pixels
F-measure curve	$F_m=\frac{(1+{\beta }^2)P·R}{{\beta }^2·P+R}$ ${\beta }^2$ is set to 0.3 to emphasize the effect of $P$	F-measure is computed by the weighted harmonic mean of precision and recall, which can reflect the quality of detection
DICE score	$DICE=\frac{2\times |S_M\cap G_T|}{|S_M|+|G_T|}$	DICE score measures the similarity between the predicted map and the ground truth
Sensitivity score	$Sen.=\frac{TP}{TP+FN}$	Sensitivity score measures the rate of missed detection
Specificity score	$Spec.=\frac{TN}{FP+TN}$	Specificity score measures the rate of false detection
Mean absolute error (MAE) score	$MAE=\frac{1}{W\times H}\sum _1^W\sum _1^H|S_M-G_T|$ $W$ and $H$ denote the width and height of the image, respectively	MAE score indicates the similarity between the segmentation map and the ground truth
Area under curve (AUC) score	$AUC=\frac{{\sum }_{t_0\in E^0}{\sum }_{t_1\in E^1}\mathrm{I}[f\left(t_0\right)-f\left(t_0\right)]}{|E^0|·|E^1|}$ $E^0$ and $E^1$ denote the set of negative and positive examples, respectively	AUC score gives an intuitive indication of how well the segmentation map predicts the true lung infection regions
Weighted F-measure (WF) score [45]	$WF=\frac{(1+{\beta }^2)WP·WR}{{\beta }^2·WP+WR}$ $WP$ and $WR$ denote the weighted precision and weighted recall, respectively	WP and WP measure the exactness and completeness, respectively
Overlapping ratio (OR) score	$OR=\frac{|S_{BM}\cap G_T|}{|S_{BM}\cup G_T|}$ $S_{BM}$ denotes the binary segmentation map	OR score measures the completeness of lung infection pixels and the correctness of non-lung infection pixels
Structure-measure (S-M) score [46]	$(1-\alpha )\times S_O(S_M,G_T)+\alpha \times S_R(S_M,G_T)$ $S_O$ and $S_R$denote the object-aware similarity and region-aware similarity, respectively	S-M score measures the structural similarity between the segmentation map and the ground truth
Execution time	Average execution time per image (in second)	All experiments were performed with the same equipment and settings