MK-ResRecon predicts missing slices with a multi-kernel texture-aware loss while IdentityRefineNet3D refines the combined 3D volume to enable accurate reconstruction from highly sparse 2D inputs.
IEEE transactions on medical imaging 34(10), 1993–2024 (2014)
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Targeted data augmentations let single-sequence 3D spine segmentation models generalize to seven unseen CT and MRI datasets with 155% average Dice gain and almost no in-domain loss.
Ensembling inpainting models with median filtering, histogram matching, pixel averaging, and lightweight U-Net refinement yields more anatomically plausible and accurate inpainted MRI regions than individual baseline models.
citing papers explorer
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MK-ResRecon: Multi-Kernel Residual Framework for Texture-Aware 3D MRI Refinement from Sparse 2D Slices
MK-ResRecon predicts missing slices with a multi-kernel texture-aware loss while IdentityRefineNet3D refines the combined 3D volume to enable accurate reconstruction from highly sparse 2D inputs.
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One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation
Targeted data augmentations let single-sequence 3D spine segmentation models generalize to seven unseen CT and MRI datasets with 155% average Dice gain and almost no in-domain loss.
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Post-Processing Methods for Improving Accuracy in MRI Inpainting
Ensembling inpainting models with median filtering, histogram matching, pixel averaging, and lightweight U-Net refinement yields more anatomically plausible and accurate inpainted MRI regions than individual baseline models.