Hybrid deep learning plus iterative optimization framework for 3D pelvic organ reconstruction from MRI that reports lower Chamfer distances and higher Dice scores than prior deep learning models.
architectural distortion
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A geometry-motion embedded neural network reconstructs 3D myocardial infarct shapes from multi-view cine MRIs, reporting Dice 0.678 and electrophysiological simulation consistency with LGE ground truth on 225 cases.
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High-Fidelity 3D Geometric Reconstruction of Pelvic Organs from MRI: A Hybrid Deep Learning and Iterative Optimization Approach
Hybrid deep learning plus iterative optimization framework for 3D pelvic organ reconstruction from MRI that reports lower Chamfer distances and higher Dice scores than prior deep learning models.
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Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI for Cardiac Digital Twins
A geometry-motion embedded neural network reconstructs 3D myocardial infarct shapes from multi-view cine MRIs, reporting Dice 0.678 and electrophysiological simulation consistency with LGE ground truth on 225 cases.