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PIVM: Diffusion-Based Prior-Integrated Variation Modeling for Anatomically Precise Abdominal CT Synthesis

arXiv 2603.22626 ↗First posted 23 Mar 2026cs.CVcited by 0

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Abstract (arXiv)

Abdominal CT data are limited by high annotation costs and privacy constraints, which hinder the development of robust segmentation and diagnostic models. We present a Prior-Integrated Variation Modeling (PIVM) framework, a diffusion-based method for anatomically accurate CT image synthesis. Instead of generating full images from noise, PIVM predicts voxel-wise intensity variations relative to organ-specific intensity priors derived from segmentation labels. These priors and labels jointly guide the diffusion process, ensuring spatial alignment and realistic organ boundaries. Unlike latent-space diffusion models, our approach operates directly in image space while preserving the full Hounsfield Unit (HU) range, capturing fine anatomical textures without smoothing. Source code is available at https://github.com/BZNR3/PIVM.

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References · 15

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2020Denoising Diffusion Implicit Modelscited by 9,655
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  5. 2017Improved Training of Wasserstein GANscited by 2,223
  6. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
  7. 2016Least Squares Generative Adversarial Networkscited by 1,000
  8. 2022TotalSegmentator: robust segmentation of 104 anatomical structures in CT imagescited by 597
  9. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
  10. 2020AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?cited by 222
  11. 2018Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networkscited by 146
  12. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstructioncited by 117
3 more
  1. 2024MAISI: Medical AI for Synthetic Imagingcited by 86
  2. 2022Conversion Between CT and MRI Images Using Diffusion and Score-Matching Modelscited by 69
  3. 2022Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstructioncited by 64

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  1. 2023Semantic Image Synthesis for Abdominal CT
  2. 2024GuideGen: A Text-Guided Framework for Paired Full-torso Anatomy and CT Volume Generation
  3. 2026Uncertainty as a Proxy for Semantic Correctness in Diffusion-Based Medical Image Synthesis
  4. 2024Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models
  5. 2026Unsupervised Anatomical Feature Learning via Diffusion Models: Enhanced Medical Image Segmentation with Denoising Diffusion Probabilistic Models
  6. 2026Abdominal Ultrasound Simulation from Semantic Labels using Paired Label-to-Physics-Based Image Translation
  7. 2024Label-efficient multi-organ segmentation with a diffusion model
  8. 2024CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation