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Table 3 Multiple comparison of CMG Net and the order four nets of segmentation time

From: Utility of a novel integrated deep convolutional neural network for the segmentation of hip joint from computed tomography images in the preoperative planning of total hip arthroplasty

Disease (I) NET (J) NET Mean difference (I − J) Std. error Sig 95% confidence interval
Lower bound Upper bound
Multiple comparisons
Dependent variable: TIME
Least significance difference (LSD)
ONFH CMG net FCN − 10.66 0.36 < 0.05 − 11.37 − 9.95
2D UNET − 9.87 0.36 < 0.05 − 10.58 − 9.16
2.5D UNET − 31.55 0.36 < 0.05 − 32.26 − 30.84
3D UNET − 41.33 0.36 < 0.05 − 42.04 − 40.62
DDH CMG net FCN − 10.22 0.22 < 0.05 − 10.66 − 9.78
2D UNET − 10.15 0.22 < 0.05 − 10.59 − 9.71
2.5D UNET − 32.52 0.22 < 0.05 − 32.96 − 32.09
3D UNET − 40.69 0.22 < 0.05 − 41.13 − 40.25
FNF CMG net FCN − 9.84 0.12 < 0.05 − 10.07 − 9.61
2D UNET − 9.81 0.12 < 0.05 − 10.04 − 9.58
2.5D UNET − 31.43 0.12 < 0.05 − 31.66 − 31.20
3D UNET − 39.93 0.12 < 0.05 − 40.16 − 39.69
OA CMG net FCN − 8.63 0.32 < 0.05 − 9.25 − 8.00
2D UNET − 10.51 0.32 < 0.05 − 11.13 − 9.88
2.5D UNET − 29.66 0.32 < 0.05 − 30.29 − 29.04
3D UNET − 40.06 0.32 < 0.05 − 40.69 − 39.44
NORMAL CMG net FCN − 8.97 0.32 < 0.05 − 9.59 − 8.34
2D UNET − 10.83 0.32 < 0.05 − 11.46 − 10.21
2.5D UNET − 30.33 0.32 < 0.05 − 30.96 − 29.70
3D UNET − 40.23 0.32 < 0.05 − 40.86 − 39.60