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Related Experiment Video

Updated: Feb 7, 2026

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Anatomically constrained liver CT anomaly detection using healthy priors with diffusion-based inpainting.

Eshan Joshi1, Yongyi Shi2, Albert Montillo1

  • 1University of Texas Southwestern Medical Center.

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Summary

This study introduces a new four-stage pipeline for detecting liver lesions on CT scans. The method uses anatomical constraints and a healthy liver prior for improved anomaly detection, aiding in CT triage.

Keywords:
anomaly detectioncomputed tomographydiffusion modelsgenerative artifcial intelligenceunsupervised learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Detecting subtle liver lesions on CT scans is difficult due to variations in tumor characteristics and imaging protocols.
  • Fully supervised segmentation methods require extensive manual annotation, limiting scalability.
  • Existing anomaly detection methods lack anatomical constraints, leading to boundary instability and sensitivity to image variations.

Purpose of the Study:

  • To develop an anatomically constrained, four-stage pipeline for anomaly detection in liver CT scans.
  • To improve the accuracy and generalizability of liver lesion detection without extensive manual annotation.
  • To create a data-efficient method for localizing liver lesions for CT triage and prioritization.

Main Methods:

  • A denoising diffusion probabilistic model (DDPM) was trained on healthy liver CT slices to establish a normal appearance prior.
  • Diffusion-based inpainting generated pseudo-normal liver images within an automatically segmented liver mask.
  • An encoder-decoder model reconstructed healthy liver tissue using paired original and inpainted images.
  • A liver-scoped difference map between original and reconstructed images served as the anomaly score.

Main Results:

  • The method achieved a Dice score of 0.596, IoU of 0.482, and AUROC of 0.861 on abnormal CT scans.
  • Performance improved with lesion size, reaching a Dice score of 0.796 for the largest quartile of lesions.
  • The approach demonstrated data-efficient liver lesion localization, outperforming methods without anatomical constraints.
  • The 95th percentile Hausdorff distance was 80.5 pixels.

Conclusions:

  • Anatomically constrained anomaly detection with a stable healthy prior enhances liver lesion localization in CT scans.
  • The proposed four-stage pipeline offers a data-efficient and scalable solution for detecting subtle liver lesions.
  • This method shows promise for improving CT triage and prioritization in routine clinical practice.