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. Author manuscript; available in PMC: 2024 Apr 1.
Published in final edited form as: Med Image Anal. 2023 Jan 2;85:102731. doi: 10.1016/j.media.2022.102731

Table 1. Segmentation accuracy metrics presented separately for younger and older fetuses. The metrics were computed separately for each label; this table presents mean ± standard deviation over all labels. Best results for each metric are in bold. We used paired t-tests to compare our proposed method with every other method. Asterisks in this table denote significantly better results for the proposed method than all other methods.

(at a significant threshold of p < 0.001). In the Method column in this table, T.L. stands for transfer learning.

Dataset Method DSC HD95 (mm) ASSD (mm)
Younger fetuses nnU-Net 0.872 ± 0.063 0.99 ± 0.11 0.26 ± 0.14
Generalized Dice 0.845 ± 0.087 1.09 ± 0.12 0.32 ± 0.26
Focal loss 0.839 ± 0.080 1.15 ± 1.20 0.30 ± 0.19
iMAE 0.865 ± 0.075 1.06 ± 0.15 0.26 ± 0.17
Training on clean labels (without T.L.) 0.863 ± 0.068 1.09 ± 0.14 0.28 ± 0.16
Training on clean labels (with T.L.) 0.866 ± 0.062 1.03 ± 0.13 0.27 ± 0.17
Standard label smoothing 0.833 ± 0.084 1.08 ± 0.17 0.34 ± 0.21
SVLS 0.843 ± 0.074 1.07 ± 0.17 0.30 ± 0.18
DeepLab 0.851 ± 0.072 1.11 ± 0.15 0.30 ± 0.15
UNet++ 0.866 ± 0.060 1.02 ± 0.14 0.27 ± 0.15
Proposed method 0.893 ± 0.066 0.94 ± 0.13 0.23 ± 0.13

Older fetuses nnU-Net 0.896 ± 0.066 0.98 ± 0.11 0.36 ± 0.12
Generalized Dice 0.866 ± 0.070 1.16 ± 0.11 0.46 ± 0.15
Focal loss 0.861 ± 0.068 1.16 ± 0.16 0.42 ± 0.16
iMAE 0.880 ± 0.064 1.09 ± 0.17 0.41 ± 0.20
Training on clean labels (without T.L.) 0.877 ± 0.073 1.12 ± 0.14 0.40 ± 0.18
Training on clean labels (with T.L.) 0.880 ± 0.070 1.04 ± 0.15 0.40 ± 0.20
Standard label smoothing 0.853 ± 0.071 1.16 ± 0.12 0.39 ± 0.23
SVLS 0.856 ± 0.077 1.10 ± 0.13 0.37 ± 0.27
DeepLab 0.865 ± 0.074 1.21 ± 0.19 0.43 ± 0.26
UNet++ 0.885 ± 0.070 1.08 ± 0.16 0.38 ± 0.23
Proposed method 0.916 ± 0.059 0.94 ± 0.13 0.25 ± 0.09
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