Figure 1.
Illustration of the algorithm for the creation of an average space/template, emphasizing schematically on the different spaces involved in the transformations.
1a. First Iteration: 1st Step: Non-rigid registration of cohort to the candidate target; 2nd Step: Averaging of the nonrigid 10 mm transformation (blue arrow); 3rd Step: Inversion of the average nonrigid 10 mm transformation (red arrow); 4th Step: Composite transformation (nonrigid 2.5 mm+inverted average 10 mm); 5th Step: Averaging of the MRIs in average (AV) space. 1b. Second Iteration: 1st Step: Non-rigid registration of cohort to the new candidate (AV space); 2nd Step: Averaging of the nonrigid 10 mm transformation (blue arrow); 3rd Step: Inversion of the average nonrigid 10 mm transformation (red arrow); 4th Step: Composite transformation (nonrigid 2.5 mm+inverted average 10 mm); 5th Step: Averaging of the MRIs in New AV space.
Table 1.
Neonatal Templates based on different candidate target and different prior MRIs.
Table 2.
MPNAs based on different templates and different ALBERTs.
Figure 2.
Automatic segmentation using different groups of ALBERTs and decision fusion.
Comparison with manual gold standard ALBERT.
Figure 3.
Average space templates and corresponding MPNAs.
All MPNAs shown here are derived from fusion of all remaining 19 transformed ALBERTs. Only the template creation differs in terms of the initial candidate target (see Figure 1): term-born for MPNA_01 MPNA_02 and MPNA_04, preterm for 03; and in terms of the MRIs averaged to create the template space: all remaining 19 for MPNA_01; all 15 preterms for MPNA_02; all remaining 14 preterms for MPNA_03, and all remaining 4 terms for MPNA_04.
Figure 4.
Validation of different approaches for automatic segmentation of the newborn brain.
Dice measurements for 50 ROIs, either fusing anatomical prior information from various combinations of ALBERTs or propagating labels of various MPNAs. Only the analytical results of the approaches that performed best are displayed. For translating the numbers into anatomical region names, see Table 4.
Table 3.
Validation results by means of SI measurements of the different templates, different ALBERTs of Optimum Segmentation and different fusion approaches.
Table 4.
Dice statistics for 50 ROIs with fusion approach ALBERTs_19 for preterms.
Table 5.
Dice statistics for 50 ROIs with fusion approach ALBERTs_14 for preterms.
Table 6.
Dice statistics for 50 ROIs with MPNA_04 approach for preterms.
Table 7.
Dice statistics for 50 ROIs with fusion approach ALBERTs_19 for terms.
Table 8.
Dice statistics for 50 ROIs with fusion approach ALBERTs_4 for terms.
Table 9.
Dice statistics for 50 ROIs with MPNA_04_Terms approach for terms.
Figure 5.
Performance of MPNA_04 in the automatic segmentation of three randomly chosen unlabeled developing brains at various ages, which did not form part of the cohort of priors.
The segmentation is the result of a single step registration and propagation of the MPNA.