C2W-Tune transfers weights from a pre-trained LA cavity model to achieve higher accuracy in thin atrial wall segmentation, raising Dice from 0.623 to 0.814 on the 2018 LA challenge dataset.
A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging,
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SPADE-LDM conditional synthesis from composite semantic masks produces realistic 3D LGE MRI that raises LA cavity Dice from 0.908 to 0.936.
MAML with auxiliary cavity tasks and boundary loss improves 5-shot LA wall segmentation over standard fine-tuning (DSC 0.54 vs 0.48) and nears fully supervised performance at 20 shots.
citing papers explorer
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C2W-Tune: Cavity-to -Wall Transfer Learning for Thin Atrial Wall Segmentation in 3D LGE-MRI
C2W-Tune transfers weights from a pre-trained LA cavity model to achieve higher accuracy in thin atrial wall segmentation, raising Dice from 0.623 to 0.814 on the 2018 LA challenge dataset.
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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.
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Few-Shot Left Atrial Wall Segmentation in 3D LGE MRI via Meta-Learning
MAML with auxiliary cavity tasks and boundary loss improves 5-shot LA wall segmentation over standard fine-tuning (DSC 0.54 vs 0.48) and nears fully supervised performance at 20 shots.