Table 2:
Comparison of performance metrics of segmentations for different CNN modelsa
| Model | Dice | Precision | Sensitivity |
|---|---|---|---|
| LOWB | 6.5 (0.3–20.9) | 5.7 (0.3–32.7) | 8.5 (0.3–28.5) |
| ADCb | 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)b | 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) |
a
All metrics are denoted in percentages as median (IQR). Of the nonensemble models, significant differences in Dice, precision, and sensitivity were found (P < .001). The ensemble models, E2 and E3, were superior to all other models (P < .001).
b
Excludes 1 subject with an automatically segmented lesion volume of zero because precision is undefined in this circumstance.