NV-Generate-BodyMask is a 3D latent diffusion model that generates whole-body CT segmentation masks with precise control over organ anatomy, tumor burden, and patient demographics. It operates in the compressed latent space of a frozen mask autoencoder, producing 256³ volumetric label maps conditioned on a 19‑dimensional vector via Adaptive Group Normalization and cross‑attention. Trained on 8,571 masks from 26 cohorts, the model achieves high Spearman correlations for organ size, excellent tumor recall for several lesion types, and strong demographic alignment.
arXiv:2607. 02998v2 Announce Type: replace-cross Abstract: Controllable generative models of 3D medical images can synthesize volumes with specified clinical attributes, but this demands samples that are simultaneously high-fidelity, natively 3D, and faithful to the requested conditioning.
arXiv:2607. 02998v1 Announce Type: cross Abstract: Controllable generative models of 3D medical images can synthesize volumes with specified clinical attributes, but this demands samples that are simultaneously high-fidelity, natively 3D, and faithful to the requested conditioning.
Self-supervised pretraining enables transferable representations for medical imaging, yet most CT encoders remain biased toward coarse semantic understanding, limiting their sensitivity to fine-graine...
arXiv:2607. 25164v1 Announce Type: cross Abstract: A CT examination captures multiple organs, but many biomedical questions concern abnormalities, prognosis, or longitudinal change in a specific organ.
The paper introduces MedSegLatDiff, a diffusion-based framework that combines a variational autoencoder (VAE) with a latent diffusion model for medical image segmentation. By compressing images into a low-dimensional latent space, the method reduces noise and speeds up training, while a weighted cross‑entropy loss preserves tiny structures such as small nodules. Evaluated on ISIC‑2018, CVC‑Clinic, and LIDC‑IDRI datasets, MedSegLatDiff achieves state‑of‑the‑art Dice and IoU scores, generates diverse segmentation hypotheses, and produces confidence maps that enhance interpretability and reliability for clinical deployment.
arXiv:2608.24364v1 Announce Type: new Abstract: Self-supervised pretraining enables transferable representations for medical imaging, yet most CT encoders remain biased toward coarse semantic underst...
arXiv:2609.21698v1 Announce Type: new Abstract: Deep learning segmentation of renal tumors requires large annotated datasets, yet clinical deployments typically offer only a handful of tumor-positive...
arXiv:2606. 19651v1 Announce Type: new Abstract: Three-dimensional (3D) brain MRI is central to clinical neurology and neuro-oncology, where generative models could augment under-represented cohorts, simulate disease trajectories, and support privacy-preserving data sharing.
arXiv:2505. 17338v3 Announce Type: replace-cross Abstract: Photorealistic volumetric rendering of CT scans greatly benefits clinical workflows, yet neural approaches such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) require prohibitive per-scan optimization (hours for NeRF, about 30 minutes for 3DGS), making them impractical in clinical settings.
MATS is a multi-agent framework that enables text‑controlled synthesis of realistic tumors across different organs. It profiles the morphological distribution of a target disease, constructs a generation plan that emphasizes underrepresented subtypes, and uses a two‑stage diffusion process to create anatomically valid tumor masks and textures. A closed‑loop reflective mechanism checks attribute alignment at multiple stages and guides re‑generation when necessary, leading to improved downstream segmentation performance on several medical imaging benchmarks.
Merlin Plus is a new, large-scale CT dataset that provides radiologist‑created tumor masks for nine different organs, adding 1,153 per‑voxel masks and longitudinal metadata to the existing Merlin collection. The dataset was built using a report‑based active‑learning framework, where radiology reports flag tumor cases, a segmentation model generates initial masks, and radiologists review and correct them, thereby reducing annotation effort while preserving high quality. The added longitudinal data enables temporal modeling of cancer progression, supporting scalable multi‑organ cancer detection, segmentation, and longitudinal analysis in CT.
The paper introduces $K$-NeAS, a scalable neural architecture for multi-material CT reconstruction that replaces separate material networks with a shared latent backbone and a differentiable $K$-material soft selector. It automates attenuation bounds using a Gaussian Mixture Model and adds a scheduled auxiliary floater loss to reduce geometric hallucinations in sparse-view settings. Evaluated on four clinical CBCT datasets, $K$-NeAS achieves higher 3D volumetric fidelity—up to a 1.88 dB PSNR gain over a single-material baseline—and shows improved robustness under extreme sparsity, outperforming baselines by up to 1.17 dB.