VesselSim trains a 3D vessel segmentation model exclusively on 16,500 synthetic angiographic volumes generated by stochastic branching simulation and achieves competitive zero-shot performance on real clinical datasets via test-time mask reconstruction adaptation.
Sam-med3d: Towards general- purpose segmentation models for volumetric medical images
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
years
2026 8verdicts
UNVERDICTED 8representative citing papers
ESICA delivers state-of-the-art accuracy on a five-modality 3D medical segmentation benchmark while offering a compact variant with far fewer parameters.
CrossPan benchmark shows cross-sequence MRI domain shifts cause pancreas segmentation models to fail catastrophically, establishing sequence generalization as the primary barrier to clinical deployment over center variability or architecture choices.
PGE-SAM adds a Prompt Guidance Generator, multi-scale feature interaction, and foreground reconstruction loss to SAM for better interactive segmentation on degraded images, plus a new DM-Seg benchmark.
A text-guided multi-encoder U-Net with alignment loss, heatmap calibration, and confidence-gated cross-attention refiner sets new state-of-the-art 3D prostate lesion segmentation performance on the PI-CAI dataset.
TSegAgent performs zero-shot tooth instance segmentation and identification on 3D dental scans via multi-view foundation models plus explicit dental-arch geometric reasoning.
LETT-NeXt uses RECIST line prompts in a cropped MedNeXt-v2 encoder-decoder to predict 3D lesion masks, reaching DSC 73.9 on hidden test data for a CVPR 2026 segmentation competition.
An attention-based fusion model combining semi-supervised CT segmentation, radiomics, and clinical features predicts metastatic recurrence, overall survival, and disease-free survival in HPV+ oropharyngeal cancer with AUCs of 88.2%, 79.2%, and 78.1% on an internal cohort of 397 patients.
citing papers explorer
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VesselSim: learning 3D blood vessel segmentation without expert annotations
VesselSim trains a 3D vessel segmentation model exclusively on 16,500 synthetic angiographic volumes generated by stochastic branching simulation and achieves competitive zero-shot performance on real clinical datasets via test-time mask reconstruction adaptation.
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ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation
ESICA delivers state-of-the-art accuracy on a five-modality 3D medical segmentation benchmark while offering a compact variant with far fewer parameters.
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CrossPan: A Comprehensive Benchmark for Cross-Sequence Pancreas MRI Segmentation and Generalization
CrossPan benchmark shows cross-sequence MRI domain shifts cause pancreas segmentation models to fail catastrophically, establishing sequence generalization as the primary barrier to clinical deployment over center variability or architecture choices.
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PGE-SAM: Prompt-Guided Feature Enhancement for Interactive Segmentation under Degradation
PGE-SAM adds a Prompt Guidance Generator, multi-scale feature interaction, and foreground reconstruction loss to SAM for better interactive segmentation on degraded images, plus a new DM-Seg benchmark.
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Align then Refine: Text-Guided 3D Prostate Lesion Segmentation
A text-guided multi-encoder U-Net with alignment loss, heatmap calibration, and confidence-gated cross-attention refiner sets new state-of-the-art 3D prostate lesion segmentation performance on the PI-CAI dataset.
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TSegAgent: Zero-Shot Tooth Segmentation via Geometry-Aware Vision-Language Agents
TSegAgent performs zero-shot tooth instance segmentation and identification on 3D dental scans via multi-view foundation models plus explicit dental-arch geometric reasoning.
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LETT-NeXt: A Lightweight RECIST-Guided Model for 3D CT Lesion Segmentation
LETT-NeXt uses RECIST line prompts in a cropped MedNeXt-v2 encoder-decoder to predict 3D lesion masks, reaching DSC 73.9 on hidden test data for a CVPR 2026 segmentation competition.
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AMO-ENE: Attention-based Multi-Omics Fusion Model for Outcome Prediction in Extra Nodal Extension and HPV-associated Oropharyngeal Cancer
An attention-based fusion model combining semi-supervised CT segmentation, radiomics, and clinical features predicts metastatic recurrence, overall survival, and disease-free survival in HPV+ oropharyngeal cancer with AUCs of 88.2%, 79.2%, and 78.1% on an internal cohort of 397 patients.