Table 2:

Comparison of Performance Metrics of Segmentations for Different CNN Models. Of the non-ensemble models, significant differences in Dice, precision and sensitivity were found (p<0.0001). The ensemble models, E2 and E3, were superior to all other models (p<0.0001).

Model	Dice	Precision	Sensitivity
LOWB	6.5 [0.3-20.9]	5.7 [0.3-32.7]	8.5 [0.3-28.5]
ADC†	56.4 [27.1-75.4]	59.4 [22.3-78.4]	58.2 [32.7-78.9]
DWI	72.3 [46.2-82.5]	73.0 [38.3-88.1]	84.0 [62.4-90.8]
ADC+LOWB	76.5 [51.9-86.1]	78.1 [47.2-88.8]	79.2 [66.6-89.7]
DWI+LOWB	76.7 [58.4-85.4]	79.4 [52.0-89.8]	83.0 [64.8-90.6]
DWI+ADC	79.0 [57.1-86.4]	79.0 [62.1-90.5]	82.6 [68.4-91.4]
DWI+ADC+LOWB	78.9 [56.2-86.2]	77.4 [55.0-89.8]	83.4 [71.3-91.8]
E2 (DWI+ADC)	82.0 [62.9-88.1]	82.0 [65.1-92.6] †	84.1 [71.0-92.6]
E3 (DWI+ADC+LOWB)	82.2 [64.9-88.9]	83.2 [67.7-93.3]	83.9 [71.9-92.4]

All metrics denoted in % as median [Interquartile Range]. CNN=Convolutional Neural Network, E2=Ensemble of 5 CNNs trained on DWI+ADC, E3=Ensemble of 5 CNNs trained on DWI+ADC+LOWB, LOWB=Low b-value diffusion-weighted image.

^†Excludes one subject with automatically segmented lesion volume of zero since precision is undefined in this circumstance.