  This study evaluates the performance of conventional SyN ANTs and
learning-based registration methods in the context of pediatric neuroimaging,
specifically focusing on intrasubject deformable registration. The comparison
involves three approaches: without (NR), with rigid (RR), and with rigid and
affine (RAR) initializations. In addition to initialization, performances are
evaluated in terms of accuracy, speed, and the impact of age intervals and sex
per pair. Data consists of the publicly available MRI scans from the Calgary
Preschool dataset, which includes 63 children aged 2-7 years, allowing for 431
registration pairs. We implemented the unsupervised DL framework with a U-Net
architecture using DeepReg and it was 5-fold cross-validated. Evaluation
includes Dice scores for tissue segmentation from 18 smaller regions obtained
by SynthSeg, analysis of log Jacobian determinants, and registration pro-rated
training and inference times. Learning-based approaches, with or without linear
initializations, exhibit slight superiority over SyN ANTs in terms of Dice
scores. Indeed, DL-based implementations with RR and RAR initializations
significantly outperform SyN ANTs. Both SyN ANTs and DL-based registration
involve parameter optimization, but the choice between these methods depends on
the scale of registration: network-based for broader coverage or SyN ANTs for
specific structures. Both methods face challenges with larger age intervals due
to greater growth changes. The main takeaway is that while DL-based methods
show promise with faster and more accurate registrations, SyN ANTs remains
robust and generalizable without the need for extensive training, highlighting
the importance of method selection based on specific registration needs in the
pediatric context. Our code is available at
https://github.com/neuropoly/pediatric-DL-registration
