By the presented approach for a complete MRI based 3D segmentation of the VT at a functional state, we were able to obtain a high-detailed model.
Images with segmentation errors were discarded from the presented study.
The presented experimental results highlight the challenges that VIO data present for vessel segmentation methods.
The segmentation of the bony labyrinth represents a crucial step in the presented algorithm.
The presented method can detect this difference; however, this difference is often not found by previous methods based on genome segmentation because they are indirect differential analysis methods.
For a good quality segmentation, ρ=1, whereas for a poor quality segmentation, ρ=0, which needs to be enhanced by the fine segmentation to be presented in the next module.
The mean tissue volumes with 95% CL as determined by the three segmentation approaches are presented in Table 4. Volumes determined by the automated program exhibited minimal differences from manually segmented volumes.
When these differences are corrected by using the same segmentation algorithms on both devices, the remaining differences are small, as shown by the present study.
Missed class time: The missed class time refers to the amount of time that a class is present but not labeled by the segmentation system in segments where the number of system classes is lower than the number of reference classes.
Mean values for (or in the case of three variables, percent agreement with) each of the variables used in the segmentation analysis, by segment, are presented in Tables 1, 2, 3 and 4. The between-segment differences on all of these variables, as ascertained by ANOVA or chi-square tests, were significant at p<.001.
