AbdomenGen: Sequential Volume-Conditioned Diffusion Framework for Abdominal Anatomy Generation

2026-04-14 · Yubraj Bhandari, Lavsen Dahal, Paul Segars, Joseph Y. Lo arxiv

Computational phantoms are widely used in medical imaging research, yet current systems to generate controlled, clinically meaningful anatomical variations remain limited. We present AbdomenGen, a sequential volume-conditioned diffusion framework for controllable abdominal anatomy generation. We introduce the \textbf{Volume Control Scalar (VCS)}, a standardized residual that decouples organ size from body habitus, enabling interpretable volume modulation. Organ masks are synthesized sequentially, conditioning on the body mask and previously generated structures to preserve global anatomical coherence while supporting independent, multi-organ control. Across 11 abdominal organs, the proposed framework achieves strong geometric fidelity (e.g., liver dice $0.83 \pm 0.05$), stable single-organ calibration over $[-3,+3]$ VCS, and disentangled multi-organ modulation. To showcase clinical utility with a hepatomegaly cohort selected from MERLIN, Wasserstein-based VCS selection reduces distributional distance of training data by 73.6\% . These results demonstrate calibrated, distribution-aware anatomical generation suitable for controllable abdominal phantom construction and simulation studies.

📄 PDF Abstract BibTeX arXiv:2604.12969

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VoxScene: Anchor-Conditioned Voxel Diffusion for Indoor Scene Arrangement

2026-05-16 · Haotian Mao, Yuhan Huang, Jiatao Lin, Yang Zhao 외 arxiv

We present VoxScene, a novel anchor-conditioned voxel diffusion framework tailored for 3D scene synthesis. Current data-driven layout generation techniques typically rely on bounding proxies or implicit representations, …

Anatomically-conditioned Latent Diffusion Model for Data-Efficient Few-Shot Cross-Domain 3D Glioma MRI Synthesis

2026-06-24 · Salman Shaik, Truong Thanh Hung Nguyen, Hung Cao arxiv

Accurate classification of diffuse gliomas is often hindered by domain shifts across centers and a lack of large, annotated datasets. We propose the Anatomically-conditioned Latent Diffusion Model (ALDM), a novel framewo…

Data Augmentation

TRACE: Temporally Reliable Anatomically-Conditioned 3D CT Generation with Enhanced Efficiency

2025-07-01 · Minye Shao, Xingyu Miao, Haoran Duan, Zeyu Wang 외 arxiv

3D medical image generation is essential for data augmentation and patient privacy, calling for reliable and efficient models suited for clinical practice. However, current methods suffer from limited anatomical fidelity…

Medical Image Generation Computational Efficiency Data Augmentation

SALIENT: Frequency-Aware Paired Diffusion for Controllable Long-Tail CT Detection

2026-02-26 · Yifan Li, Mehrdad Salimitari, Taiyu Zhang, Guang Li 외 arxiv

Detection of rare lesions in whole-body CT is fundamentally limited by extreme class imbalance and low target-to-volume ratios, producing precision collapse despite high AUROC. Synthetic augmentation with diffusion model…

Controlled Latent Diffusion Models for 3D Porous Media Reconstruction

2025-03-31 · Danilo Naiff, Bernardo P. Schaeffer, Gustavo Pires, Dragan Stojkovic 외

Three-dimensional digital reconstruction of porous media presents a fundamental challenge in geoscience, requiring simultaneous resolution of fine-scale pore structures while capturing representative elementary volumes. …