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March 3, 2026
DeepVBM: A fully automatic and efficient voxel-based morphometry via deep learning-based segmentation and registration methods
PS
Pei-Mao Sun
TH
Teng-Yi Huang
National Taiwan University of Science and Technology
TC
Tzu-Chao Chuang
National Sun Yat-sen University
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Key Points
Automatic voxel-based morphometry improves brain imaging efficiency and accuracy, enabling faster analysis.
Deep learning segmentation methods lead to more accurate image processing metrics, enhancing diagnostic potential.
Implementation of registration techniques in the model aids in better alignment of brain scans across subjects, boosting reliability.
This advancement may enable widespread clinical adoption while addressing limitations in traditional methods.
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Sun et al. (Tue,) studied this question.
synapsesocial.com/papers/69a7662cbadf0bb9e87dbfdc
https://doi.org/https://doi.org/10.1016/j.mri.2026.110637
DeepVBM: A fully automatic and efficient voxel-based morphometry via deep learning-based segmentation and registration methods | Synapse