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Figure 4 Mean localization error for ten realizations, for varying number of nodes in network, using error-free ranges.
Fig. 10 Comparison of different approaches in a simulated case: for each value of σ, we run 200 experiments to compute the mean localization error.
Our method achieved the 1st place in the MICCAI challenge on automatic localization and segmentation of IVDs from multi-modality MR images, with a mean segmentation Dice coefficient of 91.2% and a mean localization error of 0.62 mm.
Over all source locations and methods, the mean localization error when using the inner skull boundary was significantly lower than when using the scalp surface (scalp/inner skull = 9.08 mm/6.76 mm P < 0.0002).
The number of iterations required for localization is reduced since we do the localization cluster by cluster or mesh by mesh. Figure 5 Mean localization error for ten realizations, for varying number of nodes in network, using error-induced ranges.
Finally, with 3×2 cells hierarchical method (yellow line), we incur a mean localization error that is always higher than, or at most the same as, WkNN, but our method is better than spatial sparsity, except for very high noise cases.
It treats fixture layout as a multi-objective optimization problem, which considers both the mean and the variation of the mean localization errors of the machining features in a set-up, and the distance of the location points among the locators to ensure the location is stable and reliable.
Mean shows patient average of mean localization errors among fractions for each patient.
Localization errors Magnitude errors were calculated in the same way as in Experiment 1, and the mean localization errors are shown in Fig. 6.
In the case of considering noise-free EEG measurements, the mean localization errors were approximately equal to zero when using actual BSCR.
When considering noise-contaminated EEG measurements, the mean localization errors ranged from 8 to 18 mm despite the BSCRs used in the inverse calculation.
