Modality-projection universal model for comprehensive full-body medical imaging segmentation

Yixin Chen, Lin Gao, Yajuan Gao, Rui Wang, Jingge Lian, Xiangxi Meng, Yanhua Duan, Leiying Chai, Hongbin Han, Zhaoping Cheng () and Zhaoheng Xie ()
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Yixin Chen: Peking University Health Science Center, Peking University
Lin Gao: The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital
Yajuan Gao: Peking University Third Hospital
Rui Wang: Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University
Jingge Lian: Peking University Third Hospital
Xiangxi Meng: Peking University Cancer Hospital & Institute
Yanhua Duan: The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital
Leiying Chai: The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital
Hongbin Han: Peking University Health Science Center, Peking University
Zhaoping Cheng: The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital
Zhaoheng Xie: Peking University Health Science Center, Peking University

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract The integration of deep learning in medical imaging has significantly advanced diagnostic, therapeutic, and research outcomes. However, applying universal models across multiple modalities remains challenging due to inherent inter-modality variability. Here we present the Modality Projection Universal Model (MPUM), trained on 861 subjects, which dynamically adapts to diverse imaging modalities through a modality-projection strategy. MPUM achieves state-of-the-art, whole-body organ segmentation, providing rapid localization for computer-aided diagnosis and precise anatomical quantification to support clinical decision-making. A controller-based convolutional layer further enables saliency map visualization, enhancing model interpretability for clinical use. Beyond segmentation, MPUM reveals metabolic correlations along the brain-body axis and between distinct brain regions, providing insights into systemic and physiological interactions from a whole-body perspective. Here we show that this universal framework accelerates diagnosis, facilitates large-scale imaging analysis, and bridges anatomical and metabolic information, enabling discovery of cross-organ disease mechanisms and advancing integrative brain-body research.

Date: 2025
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DOI: 10.1038/s41467-025-64469-w

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