TY - JOUR
T1 - OTFPF
T2 - Optimal transport based feature pyramid fusion network for brain age estimation
AU - Fu, Yu
AU - Huang, Yanyan
AU - Zhang, Zhe
AU - Dong, Shunjie
AU - Xue, Le
AU - Niu, Meng
AU - Li, Yunxin
AU - Shi, Zhiguo
AU - Wang, Yalin
AU - Zhang, Hong
AU - Tian, Mei
AU - Zhuo, Cheng
N1 - Publisher Copyright:
© 2023
PY - 2023/12
Y1 - 2023/12
N2 - Deep neural networks have shown promise in predicting the chronological age of a healthy brain using T1-weighted magnetic resonance images (T1 MRIs). This predicted brain age has the potential to serve as a valuable biomarker for identifying development-related and aging-related disorders. In this study, we propose the Optimal Transport based Feature Pyramid Fusion (OTFPF) network for estimating brain age using T1 MRIs. The OTFPF network comprises three key modules: the Optimal Transport based Feature Pyramid Fusion (OTFPF) module, the 3D overlapped ConvNeXt (3D OL-ConvNeXt) module, and the fusion module. These modules enhance the OTFPF network's ability to comprehend the semi-multimodal and multi-level feature pyramid information of each brain, thereby improving its understanding of brain development and aging. Compared to recent state-of-the-art models, the proposed OTFPF network demonstrates faster convergence, superior performance, and enhanced interpretability. Experimental results utilizing a dataset of 12,909 MRIs from individuals aged 3–97 years demonstrate the accurate estimation of brain age by the OTFPF network, achieving a mean absolute error (MAE) of 1.846, Pearson's correlation coefficient (PCC) of 0.9941, and Spearman's rank correlation coefficient (SRCC) of 0.9802. Thorough parameter evaluations, quantitative comparison experiments, dataset-scale evaluations, cross-validations, and ablation studies convincingly demonstrate the stability, interpretability, and superiority of the OTFPF network. According to the OTFPF network, the age-related heatmaps of the brain explain the biological mechanisms underlying brain aging. Furthermore, the OTFPF network is applied to analyze datasets associated with brain disorders, effectively demonstrating its practical utility.
AB - Deep neural networks have shown promise in predicting the chronological age of a healthy brain using T1-weighted magnetic resonance images (T1 MRIs). This predicted brain age has the potential to serve as a valuable biomarker for identifying development-related and aging-related disorders. In this study, we propose the Optimal Transport based Feature Pyramid Fusion (OTFPF) network for estimating brain age using T1 MRIs. The OTFPF network comprises three key modules: the Optimal Transport based Feature Pyramid Fusion (OTFPF) module, the 3D overlapped ConvNeXt (3D OL-ConvNeXt) module, and the fusion module. These modules enhance the OTFPF network's ability to comprehend the semi-multimodal and multi-level feature pyramid information of each brain, thereby improving its understanding of brain development and aging. Compared to recent state-of-the-art models, the proposed OTFPF network demonstrates faster convergence, superior performance, and enhanced interpretability. Experimental results utilizing a dataset of 12,909 MRIs from individuals aged 3–97 years demonstrate the accurate estimation of brain age by the OTFPF network, achieving a mean absolute error (MAE) of 1.846, Pearson's correlation coefficient (PCC) of 0.9941, and Spearman's rank correlation coefficient (SRCC) of 0.9802. Thorough parameter evaluations, quantitative comparison experiments, dataset-scale evaluations, cross-validations, and ablation studies convincingly demonstrate the stability, interpretability, and superiority of the OTFPF network. According to the OTFPF network, the age-related heatmaps of the brain explain the biological mechanisms underlying brain aging. Furthermore, the OTFPF network is applied to analyze datasets associated with brain disorders, effectively demonstrating its practical utility.
KW - Brain age estimation
KW - Deep learning
KW - Feature pyramid fusion
KW - Optimal transport
UR - http://www.scopus.com/inward/record.url?scp=85165534154&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85165534154&partnerID=8YFLogxK
U2 - 10.1016/j.inffus.2023.101931
DO - 10.1016/j.inffus.2023.101931
M3 - Article
AN - SCOPUS:85165534154
SN - 1566-2535
VL - 100
JO - Information Fusion
JF - Information Fusion
M1 - 101931
ER -