Table 1.

The comparison of different methods with different evaluation metrics. The last three rows show the segmentation accuracy of our method with coarse level (CL) sparse label propagation only, CL based on the features derived by SDA, and finally integrated with domain-specific manifold regularization in the fine level (FL), respectively. The best results are bolded.

Method	Mean Dice + SD (in %)	Min Dice (in %)	Mean Hausdorff + SD (in mm)	Mean ASD + SD (in mm)
Klein et al. [2]	81.8 ± 4.3	47.3	11.7 ± 3.2	2.8 ± 1.2
Coupe et al. [9]	78.4 ± 3.6	34.2	15.8 ± 3.6	4.1 ± 1.5
CL	82.6 ± 4.8	51.4	10.6 ± 3.3	2.6 ± 1.4
SDA+CL	85.1 ± 4.1	63.2	9.6 ± 2.7	2.4 ± 1.2
SDA+CL+FL	88.3 ± 2.6	84.6	7.7 ± 2.1	1.8 ± 0.9