OBJECTIVE
This study investigates how domain-aligned self-supervised pretraining influences downstream PET/CT tumor segmentation, with a focus on disentangling the roles of domain proximity and pretraining dataset scale. Approach: We compared nnU-Net segmentation models initialized from random weights or from DINOv3 en...
Yu-Nong Lin, Shan-Song Wang, Yu-Heng Li et al.· Physics in Medicine and Biol...· 0 citations
Objective. MRI provides excellent soft-tissue contrast, but long acquisition times can cause patient discomfort and lead to motion artifacts, forcing a trade-off between spatial resolution and scan time. Diffusion-based super-resolution (SR) reconstructs high-resolution (HR) images from low-resolution (LR) inputs, but...
Results indicate that SSRL-MAR provides a robust and scalable image-domain solution for 3D brain MRI motion correction, enabling reliable structural quantification in large-scale neuroimaging studies without requiring prospectively acquired pairs or acquisition-specific calibration.
M. Safari, Shan-Song Wang, Zach Eidex et al.· IEEE Transactions on Medical...· 0 citations
Adapting a 3D vision-language foundation model for text-guided brain tumor segmentation refinement of glioma subregion segmentations suggests text-dependent contour editing rather than nonspecific post-processing, supporting further evaluation as a clinician-in-the-loop tool.
Zach Eidex, Yunyan Lin, M. Safari et al.· 0 citations
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