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Table 6 Subgroup analysis of accuracy between CMGsNET and other four nets in segmentation of severe diseases

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 Dependent variable (I) NET (J) NET Mean difference (I − J) Std. error Sig 95% confidence interval
Lower bound Upper bound
Multiple comparisons
Least significance difference (LSD)
ONFH III ASD CMG net FCN − 0.19 0.04 < 0.05 − 0.27 − 0.12
2D UNET − 0.27 0.04 < 0.05 − 0.34 − 0.19
2.5D UNET − 0.25 0.04 < 0.05 − 0.33 − 0.17
3D UNET − 0.22 0.04 < 0.05 − 0.30 − 0.14
DOC CMG net FCN 0.10 0.03 < 0.05 0.04 0.15
2D UNET 0.04 0.03 0.12 − 0.01 0.10
2.5D UNET 0.20 0.03 < 0.05 0.15 0.25
3D UNET 0.21 0.03 < 0.05 0.16 0.26
HD CMG net FCN − 1.10 0.46 < 0.05 − 2.01 − 0.19
2D UNET − 0.33 0.46 0.47 − 1.24 0.58
2.5D UNET − 5.67 0.46 < 0.05 − 6.58 − 4.76
3D UNET − 6.67 0.46 < 0.05 − 7.58 − 5.76
ONFH IV ASD CMG net FCN 0.00 0.07 0.99 − 0.14 0.14
2D UNET − 0.09 0.07 0.20 − 0.23 0.05
2.5D UNET − 0.09 0.07 0.19 − 0.23 0.04
3D UNET − 0.10 0.07 0.15 − 0.24 0.04
DOC CMG net FCN 0.14 0.03 < 0.05 0.09 0.19
2D UNET 0.05 0.03 0.05 0.00 0.10
2.5D UNET 0.21 0.03 < 0.05 0.16 0.26
3D UNET 0.23 0.03 < 0.05 0.18 0.28
HD CMG net FCN − 1.31 0.40 < 0.05 − 2.11 − 0.52
2D UNET − 0.77 0.40 0.06 − 1.57 0.02
2.5D UNET − 5.10 0.40 < 0.05 − 5.89 − 4.30
3D UNET − 7.13 0.40 < 0.05 − 7.92 − 6.34
DDH III ASD CMG net FCN − 0.12 0.05 < 0.05 − 0.23 − 0.02
2D UNET − 0.18 0.05 < 0.05 − 0.28 − 0.07
2.5D UNET − 0.37 0.05 < 0.05 − 0.47 − 0.26
3D UNET − 0.13 0.05 < 0.05 − 0.24 − 0.03
DOC CMG net FCN 0.18 0.03 < 0.05 0.11 0.24
2D UNET 0.07 0.03 < 0.05 0.01 0.13
2.5D UNET 0.22 0.03 < 0.05 0.15 0.28
3D UNET 0.26 0.03 < 0.05 0.20 0.33
HD CMG net FCN − 1.46 0.67 < 0.05 − 2.80 − 0.11
2D UNET − 0.15 0.67 0.83 − 1.49 1.20
2.5D UNET − 5.06 0.67 < 0.05 − 6.41 − 3.72
3D UNET − 6.60 0.67 < 0.05 − 7.94 − 5.25
DDH IV ASD CMG net FCN − 0.21 0.07 < 0.05 − 0.34 − 0.08
2D UNET − 0.20 0.07 < 0.05 − 0.34 − 0.07
2.5D UNET − 0.25 0.07 < 0.05 − 0.38 − 0.12
3D UNET − 0.17 0.07 < 0.05 − 0.30 − 0.04
DOC CMG net FCN 0.13 0.04 < 0.05 0.05 0.20
2D UNET 0.12 0.04 < 0.05 0.05 0.19
2.5D UNET 0.28 0.04 < 0.05 0.21 0.35
3D UNET 0.28 0.04 < 0.05 0.20 0.35
HD CMG net FCN − 1.29 0.67 0.06 − 2.64 0.07
2D UNET − 1.50 0.67 < 0.05 − 2.85 − 0.14
2.5D UNET − 5.47 0.67 < 0.05 − 6.83 − 4.12
3D UNET − 6.39 0.67 < 0.05 − 7.74 − 5.03
FHF III ASD CMG net FCN − 0.21 0.02 < 0.05 − 0.26 − 0.16
2D UNET − 0.24 0.02 < 0.05 − 0.29 − 0.20
2.5D UNET − 0.24 0.02 < 0.05 − 0.28 − 0.19
3D UNET − 0.24 0.02 < 0.05 − 0.29 − 0.19
DOC CMG net FCN 0.11 0.02 < 0.05 0.08 0.14
2D UNET 0.04 0.02 < 0.05 0.01 0.08
2.5D UNET 0.20 0.02 < 0.05 0.17 0.23
3D UNET 0.18 0.02 < 0.05 0.15 0.21
HD CMG net FCN − 1.21 0.28 < 0.05 − 1.76 − 0.67
2D UNET 0.21 0.28 0.45 − 0.34 0.75
2.5D UNET − 4.53 0.28 < 0.05 − 5.08 − 3.99
3D UNET − 6.38 0.28 < 0.05 − 6.93 − 5.84
FHF IV ASD CMG net FCN − 0.19 0.04 < 0.05 − 0.27 − 0.12
2D UNET − 0.27 0.04 < 0.05 − 0.34 − 0.19
2.5D UNET − 0.25 0.04 < 0.05 − 0.33 − 0.17
3D UNET − 0.22 0.04 < 0.05 − 0.30 − 0.14
DOC CMG net FCN 0.10 0.03 < 0.05 0.04 0.15
2D UNET 0.04 0.03 < 0.05 − 0.01 0.10
2.5D UNET 0.20 0.03 < 0.05 0.15 0.25
3D UNET 0.21 0.03 < 0.05 0.16 0.26
HD CMG net FCN − 1.10 0.46 < 0.05 − 2.01 − 0.19
2D UNET − 0.33 0.46 0.47 − 1.24 0.58
2.5D UNET − 5.67 0.46 < 0.05 − 6.58 − 4.76
3D UNET − 6.67 0.46 < 0.05 − 7.58 − 5.76