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)
14 Pith papers cite this work. Polarity classification is still indexing.
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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.
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.
CA-GCL adds global contrastive separation and clinical text augmentation to fine-grained vision-language pretraining, reducing textual embedding collapse and prompt variance in 3D medical image tasks.
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.
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.
Semi-MedRef introduces T-PatchMix, PosAug, and ITCL within a teacher-student SSL setup to preserve image-text alignment under augmentation for medical referring segmentation on QaTa-COV19 and MosMedData+.
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.
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.
citing papers explorer
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DyABD: The Abdominal Muscle Segmentation in Dynamic MRI Benchmark
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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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.
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Echo4DIR: 4D Implicit Heart Reconstruction from 2D Echocardiography Videos
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.
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CA-GCL: Cross-Anatomy Global-Local Contrastive Learning for Robust 3D Medical Image Understanding
CA-GCL adds global contrastive separation and clinical text augmentation to fine-grained vision-language pretraining, reducing textual embedding collapse and prompt variance in 3D medical image tasks.
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Knowledge Transfer Scaling Laws for 3D Medical Imaging
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.
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Diffusion Model as a Generalist Segmentation Learner
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.
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CT-Guided Spatially-varying Regularization for Voxel-Wise Deformable Whole-Body PET Registration
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.
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WoundFormer: Multi-Scale Spatial Feature Fusion for Multi-Class Wound Tissue Segmentation
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.
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SWoMo: Neuro-Symbolic World Model for Cataract Surgery Simulation
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.
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Semi-MedRef: Semi-Supervised Medical Referring Image Segmentation with Cross-Modal Alignment
Semi-MedRef introduces T-PatchMix, PosAug, and ITCL within a teacher-student SSL setup to preserve image-text alignment under augmentation for medical referring segmentation on QaTa-COV19 and MosMedData+.
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Hierarchical Feature Learning for Medical Point Clouds via State Space Model
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.
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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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CoRE: Concept-Reasoning Expansion for Continual Brain Lesion Segmentation
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.
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Label-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI
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.