Table 1 Comparison of performance of hypothalamic segmentation by four investigated network architectures in the test dataset.

From: AI-assisted quantification of hypothalamic atrophy in amyotrophic lateral sclerosis by convolutional neural network-based automatic segmentation

 

Ground truth

EfficientNetB0

Inceptionv3

ResNet50

VGG16

IoU

0.88 ± 0.03

0.87 ± 0.02

0.87 ± 0.02

0.85 ± 0.04

Precision

0.87 ± 0.05

0.79 ± 0.05

0.82 ± 0.05

0.82 ± 0.05

Recall

0.86 ± 0.05

0.93 ± 0.04

0.87 ± 0.04

0.85 ± 0.11

Dice

0.87 ± 0.03

0.85 ± 0.03

0.85 ± 0.03

0.83 ± 0.07

Prediction time per image/ms

29

31

35

42

Hypothalamic volume/cm3

0.82 ± 0.10

0.80 ± 0.10

0.96 ± 0.12

0.87 ± 0.11

0.85 ± 0.16

p-value

0.336§

1.189·10–11§

0.0003§

0.045$

  1. §The reported p-values means that paired t-test was applied as statistics.
  2. $Wilcoxon signed-rank test.