  Chronological age of healthy brain is able to be predicted using deep neural
networks from T1-weighted magnetic resonance images (T1 MRIs), and the
predicted brain age could serve as an effective biomarker for detecting
aging-related diseases or disorders. In this paper, we propose an end-to-end
neural network architecture, referred to as optimal transport based feature
pyramid fusion (OTFPF) network, for the brain age estimation with T1 MRIs. The
OTFPF consists of three types of modules: Optimal Transport based Feature
Pyramid Fusion (OTFPF) module, 3D overlapped ConvNeXt (3D OL-ConvNeXt) module
and fusion module. These modules strengthen the OTFPF network's understanding
of each brain's semi-multimodal and multi-level feature pyramid information,
and significantly improve its estimation performances. Comparing with recent
state-of-the-art models, the proposed OTFPF converges faster and performs
better. The experiments with 11,728 MRIs aged 3-97 years show that OTFPF
network could provide accurate brain age estimation, yielding mean absolute
error (MAE) of 2.097, Pearson's correlation coefficient (PCC) of 0.993 and
Spearman's rank correlation coefficient (SRCC) of 0.989, between the estimated
and chronological ages. Widespread quantitative experiments and ablation
experiments demonstrate the superiority and rationality of OTFPF network. The
codes and implement details will be released on GitHub:
https://github.com/ZJU-Brain/OTFPF after final decision.
