LiFT factorizes 3D medical volume synthesis into per-slice 2D generation and inter-slice trajectory learning, using a tri-planar drifting loss for unconditional coherence and a z-context mixer for paired translation tasks.
Conditional diffusion models for semantic 3D brain MRI synthesis.IEEE Journal of Biomedical and Health Informatics, 28(7):4084–4093, 2024
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2representative citing papers
SPADE-LDM conditional synthesis from composite semantic masks produces realistic 3D LGE MRI that raises LA cavity Dice from 0.908 to 0.936.
citing papers explorer
-
LiFT: Lifted Inter-slice Feature Trajectories for 3D Image Generation from 2D Generators
LiFT factorizes 3D medical volume synthesis into per-slice 2D generation and inter-slice trajectory learning, using a tri-planar drifting loss for unconditional coherence and a z-context mixer for paired translation tasks.
-
3D Conditional Image Synthesis of Left Atrial LGE MRI from Composite Semantic Masks
SPADE-LDM conditional synthesis from composite semantic masks produces realistic 3D LGE MRI that raises LA cavity Dice from 0.908 to 0.936.