Abstract

This study evaluates the performance of conventional SyN ANTs and learning-based reg- istration methods in the context of pediatric neuroimaging, specifically focusing on intra- subject deformable registration. The comparison involves three approaches—without (NR), with rigid (RR), and with rigid and affine (RAR) initializations. In addition to initial- ization, 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 deep learning (DL) framework with a U-Net architecture using DeepReg and it was 5-fold cross-validated. The 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. Specifically, DL-based implementations with RR and RAR initializations significantly outperform SyN ANTs. The lower Dice scores of SyN ANTs are likely due to its lack of population-based optimization, unlike the DL methods which learn optimal parameters through training. 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. Learning-based registration offers fast inference times but needs training, whereas SyN ANTs requires manual fine-tuning, with less clear guidelines, particularly for younger cohorts. Both methods face challenges with larger age intervals due to greater growth changes. Future work will extend the framework to younger populations and explore models that better separate different levels of transformations for improved local brain region registration. 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