DyABD is the first benchmark dataset for abdominal muscle segmentation in dynamic MRIs featuring exercise-induced anatomical changes and pre/post-surgery scans, where existing models achieve an average Dice score of 0.82.
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Nature methods18(2), 203–211 (2021)
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AnyTwoReg is a set-based groupwise registration method that achieves zero-shot generalization across variable-length and variable-contrast cardiac MRI sequences by using permutation-invariant feature aggregation.
WING generates synthetic CT from MRI/CBCT by decomposing the target into lung, soft-tissue, and bone windows, fusing them via a differentiable soft-fusion operator, and refining with a Transformer, achieving state-of-the-art results on SynthRAD2025.
MFASSL adds mirror-paired views, a lightweight Mirror-Fusion Attention module, and reflection-consistency losses to improve SSL on bilateral data with ~2.7% extra parameters.
Echo4DIR reconstructs continuous 4D cardiac geometry from sparse 2D echocardiography videos using implicit representations, epipolar feature fusion, self-supervised domain adaptation, and radial SDF alignment to achieve up to 98.35% Dice overlap.
A teacher–student semi-supervised framework with alignment-preserving patch mixing, position-aware text augmentation, and positional contrastive learning improves medical referring segmentation at low label ratios.
Transfer-aware data allocation derived from observed power-law scaling laws for asymmetric knowledge transfer in 3D medical imaging outperforms standard proportional sampling by up to 58% and generalizes to new budgets.
DiGSeg repurposes diffusion U-Nets as generalist segmentation learners by conditioning on image-mask latents and multi-scale CLIP text features, achieving strong cross-domain performance.
CT-guided voxel-wise regularization for the displacement field improves whole-body cross-tracer PET registration over global regularization baselines on a 296-patient dataset.
CHIS steers pretrained diffusion models to generate histopathology images aligned with input structural masks via frequency-domain structural initialization and wavelet-based textural modulation without any training on annotated data.
WoundFormer modifies SegFormer with a spatially-preserving multi-scale aggregation head for multi-class wound tissue segmentation, reporting 81.9% Dice on the WoundTissueSeg dataset with gains over baselines.
SWoMo decouples symbolic rule-based motion modeling via scene graphs from visual realism via diffusion models, trained through inverse pairing of real cataract surgery videos reconstructed in the simulator for sim-to-real translation.
Presents an SSM-based hierarchical feature learning method for medical point clouds that reports superior performance on classification, completion, and segmentation using a new dataset MedPointS.
CA-GCL combines global contrastive learning with permutation-invariant text augmentation to deliver zero-shot 3D medical abnormality detection that is more robust to prompt changes than prior FVLP methods.
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.
CoRE aligns image tokens to a hierarchical concept library to simulate clinical reasoning for expert routing and demand-based growth in continual brain lesion segmentation, achieving SOTA on 12 tasks.
A fine-tuned 3D foundation segmentation model combined with cross pseudo supervision achieves robust liver segmentation across labeled and unlabeled multi-phase, multi-vendor MRI without spatial registration.
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Set-Based Groupwise Registration for Variable-Length, Variable-Contrast Cardiac MRI
AnyTwoReg is a set-based groupwise registration method that achieves zero-shot generalization across variable-length and variable-contrast cardiac MRI sequences by using permutation-invariant feature aggregation.