TriALS introduces a 150-case four-phase CT dataset and challenge showing top segmentation methods reach 0.754 Dice on venous phase but only 0.57 on non-contrast CT, with external validation gains up to 28%.
Huang, et al., STU-Net: Scalable and transferable medical im- age segmentation models empowered by large-scale supervised pre- trainingArXiv:2304.06716
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Camyla autonomously generates research proposals, experiments, and manuscripts in medical image segmentation, outperforming baselines on 24 of 31 recent datasets while producing 40 human-reviewed papers.
The first validated open benchmark for future liver remnant segmentation is created from 197 refined CT volumes, with a cascaded nnU-Net achieving the highest Dice score of 0.767.
CardioBench is a new public benchmark that standardizes eight echocardiography datasets into four regression and five classification tasks to evaluate foundation model generalization.
U-Mamba is a hybrid CNN-SSM architecture that outperforms prior CNN and Transformer networks on biomedical image segmentation tasks by efficiently modeling long-range dependencies.
GLeVE introduces graph-guided lesion grounding with anatomical verification and octree refinement to improve text-to-lesion alignment in 3D CT volumes.
Primus and PrimusV2 are Transformer-centric models that match or exceed nnU-Net and top CNNs on nine 3D medical segmentation datasets by enforcing attention usage.
Self-supervised pretraining on large unlabeled clinical brain MRI data improves generalization to out-of-domain clinical tasks over supervised in-domain training, with task-specific optimal objectives and limited benefits from model scaling.
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.
LesionDETR performs per-lesion set prediction on kidney CT volumes, reaching side-level AUC 0.799-0.817 and low per-lesion mAP, with segmentation masks and same-domain pretraining as dominant design choices.
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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TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT
TriALS introduces a 150-case four-phase CT dataset and challenge showing top segmentation methods reach 0.754 Dice on venous phase but only 0.57 on non-contrast CT, with external validation gains up to 28%.
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Camyla: Scaling Autonomous Research in Medical Image Segmentation
Camyla autonomously generates research proposals, experiments, and manuscripts in medical image segmentation, outperforming baselines on 24 of 31 recent datasets while producing 40 human-reviewed papers.
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Benchmarking Deep Learning for Future Liver Remnant Segmentation in Colorectal Liver Metastasis
The first validated open benchmark for future liver remnant segmentation is created from 197 refined CT volumes, with a cascaded nnU-Net achieving the highest Dice score of 0.767.
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CardioBench: Do Echocardiography Foundation Models Generalize Beyond the Lab?
CardioBench is a new public benchmark that standardizes eight echocardiography datasets into four regression and five classification tasks to evaluate foundation model generalization.
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U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation
U-Mamba is a hybrid CNN-SSM architecture that outperforms prior CNN and Transformer networks on biomedical image segmentation tasks by efficiently modeling long-range dependencies.
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GLeVE: Graph-Guided Lesion Grounding with Proposal Verification in 3D CT
GLeVE introduces graph-guided lesion grounding with anatomical verification and octree refinement to improve text-to-lesion alignment in 3D CT volumes.
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Primus: Enforcing Attention Usage for 3D Medical Image Segmentation
Primus and PrimusV2 are Transformer-centric models that match or exceed nnU-Net and top CNNs on nine 3D medical segmentation datasets by enforcing attention usage.
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Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
Self-supervised pretraining on large unlabeled clinical brain MRI data improves generalization to out-of-domain clinical tasks over supervised in-domain training, with task-specific optimal objectives and limited benefits from model scaling.
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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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Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes
LesionDETR performs per-lesion set prediction on kidney CT volumes, reaching side-level AUC 0.799-0.817 and low per-lesion mAP, with segmentation masks and same-domain pretraining as dominant design choices.
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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.