An end-to-end infant brain parcellation pipeline

Objective Accurate infant brain parcellation is crucial for understanding early brain development; however, it is challenging due to the inherent low tissue contrast, high noise, and severe partial volume effects in infant magnetic resonance images (MRIs). The aim of this study is to develop an end-to-end pipeline that enables accurate parcellation of infant brain MRIs. Methods We proposed an end-to-end pipeline that employs a two-stage global-to-local approach for accurate parcellation of infant brain MRIs. Specifically, in the global ROIs localization stage, a combination of transformer and convolution operations is employed to capture both global spatial features and fine texture features, enabling an approximate localization of the ROIs across the whole brain. In the local ROIs refinement stage, leveraging the position priors from the first stage along with the raw MRIs, the boundaries of the ROIs are refined for a more accurate parcellation. Results We utilize the Dice ratio to evaluate the accuracy of parcellation results. Results on 263 subjects from National Database for Autism Research (NDAR), Baby Connectome Project (BCP) and Cross-site datasets demonstrate the superior accuracy and robustness of our method than other competing methods. Conclusion Our end-to-end pipeline may be capable of accurately parcellating 6-month-old infant brain MRIs.