. Author manuscript; available in PMC: 2021 Oct 20.

Published in final edited form as: IEEE Int Conf Comput Vis Workshops. 2020 Mar 5;2019:10.1109/iccvw.2019.00043. doi: 10.1109/iccvw.2019.00043

Table 1.

DA results. Estimated lowerbound and upperbound for cross-modality liver segmentation with DA. Comparison of segmentation results for domain adaptation with different models. Our DALACE outperforms other methods.

DA task	DSC (std)
lowerbound	0.260 (0.072)
upperbound	0.869 (0.044)
Method	DSC (std)

CycleGAN [26]	0.721 (0.049)
TD-GAN [25]	0.793 (0.066)
DADR [24]	0.806 (0.035)
DALACE	0.847 (0.041)