Hugging Face Trending Papers
Improving Cross-Site Whole-Heart Segmentation
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arXiv Computer Vision
Aug 27
Improving Cross-Site Whole-Heart Segmentation
The paper presents a modality‑routed 3D cardiac segmentation pipeline that combines TotalSegmentator‑initialized nnU‑Netv2 models with site‑characterized, label‑preserving appearance augmentation. By analyzing measurable image properties across sites, the authors design a bias‑field plus Bezier augmentation strategy that smooths spatial intensity perturbations and remaps intensities nonlinearly, followed by class‑wise largest‑connected‑component cleanup. On held‑out validation splits, this approach raises CT mean Dice from 0.8350 to 0.9135 and MRI mean Dice from 0.7695 to 0.7830 while reducing HD95, demonstrating improved cross‑site robustness in limited‑data whole‑heart segmentation.
arXiv Computer Vision
Sep 10
Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalization
arXiv:2602.20773v2 Announce Type: replace Abstract: Purpose: Developing generalizable medical image segmentation models is challenging because imaging data are distributed across institutions and dif...
arXiv Computer Vision
Sep 1
LISynSeg: Data-Centric Label-to-Image Synthesis for Cross-Modality Whole-Heart Segmentation
arXiv:2608.31073v1 Announce Type: new Abstract: Whole-heart segmentation (WHS) in computed tomography (CT) and magnetic resonance imaging (MRI) is affected by acquisition shifts and heterogeneous car...
arXiv AI
Jun 10
++nnU-Net: Scaling nnU-Net with Prefix-Based Data Augmentation
arXiv:2606. 10713v1 Announce Type: cross Abstract: The nnU-Net has demonstrated continuous success in medical segmentation tasks, which heavily rely on the availability and diversity of annotated biomedical data.
arXiv AI
Sep 16
Beyond In-Distribution Metrics: A Systematic Out-of-Distribution Evaluation of Congenital Heart Disease Segmentation
The paper presents the first systematic evaluation of out‑of‑distribution generalization for congenital heart disease (CHD) segmentation, using the ImageCHD cohort as a held‑out target. It compares several segmentation architectures under different training regimes, showing that in‑distribution performance is a poor predictor of cross‑cohort robustness: nnU‑Net drops from 0.77 to 0.51 Dice, while SwinUNETR maintains higher performance at 0.67 Dice. Limited target‑domain adaptation with only 11 labeled ImageCHD cases boosts all SwinUNETR variants above 0.76 Dice, highlighting the importance of explicit cross‑dataset testing.
Hugging Face Trending Papers
Sep 15
Beyond In-Distribution Metrics: A Systematic Out-of-Distribution Evaluation of Congenital Heart Disease Segmentation
Congenital heart disease (CHD) diagnosis and surgical planning often require patient-specific 3D anatomical models, but manual segmentation is labor-intensive, particularly in complex anatomies. Altho...