Automated liver lesion detection in 68Ga DOTATATE PET/CT using a deep fully convolutional neural network

EJNMMI Research

Table 2 Metrics used to track model performance on the testing data at different pixel noise threshold levels

Parameter (STD)	Noise filter threshold in pixels
Parameter (STD)	5	7	10	15	20
PPV	0.69 (0.03)	0.75 (0.03)	0.82 (0.01)	0.90 (0.02)	0.94 (0.01)
Sensitivity	0.74 (0.02)	0.74 (0.03)	0.72 (0.02)	0.69 (0.02)	0.63 (0.03)
F₁ score	0.66 (0.02)	0.68 (0.02)	0.72 (0.02)	0.76 (0.01)	0.79 (0.01)
PR AUC	0.70 (0.02)	0.71 (0.02)	0.72 (0.02)	0.73 (0.02)	0.71 (0.02)

All data points are the mean of the five retrained models. F₁ is a weighted mean of PPV and sensitivity, giving a composite score for model performance. PR AUC is the area under the precision–recall curve, where precision is PPV and recall is sensitivity. Data in parentheses are the standard deviations of the mean of the five retrained models