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

Detecting subtle focal liver lesions on abdominal computed tomography (CT) is challenging in routine clinical practice, especially for small, low-contrast, or morphologically heterogeneous tumors acquired under variable protocols. While fully supervised liver tumor segmentation can achieve high accuracy, it requires pixel-level annotations that limit scalability and generalizability. Reconstruction-based anomaly detectors trained without hepatic anatomical constraints reduce label burden but are sensitive to textural variability, contrast-phase differences, and produce noisy, unstable boundaries. We introduce an anatomically constrained, four-stage pipeline for liver CT anomaly detection: (1) a denoising diffusion probabilistic model (DDPM) trained on unremarkable axial slices to learn a healthy prior; (2) diffusion-based inpainting within an automatically segmented whole-liver mask to generate pseudo-normal liver appearance; (3) a compact encoder-decoder trained with a liver-masked, mean squared error loss to reconstruct healthy liver tissue from paired original and inpainted inputs; and (4) a liver-scoped difference map between the original and reconstructed healthy CT slices as the final anomaly score for localization. Trained exclusively on > 13,000 healthy CT slices and evaluated on 1,000 abnormal CT slices from 109 Liver Tumor Segmentation (LiTS) benchmark patients, the method achieves Dice 0.596, intersection-over-union 0.482, area under the receiver operating characteristic curve 0.861, and 95th percentile Hausdorff distance 80.5 pixels (px). Performance improves with lesion size, with a Dice score of 0.796 for the largest quartile. Anchoring anomaly detection to hepatic anatomy with a stable healthy prior yields data-efficient liver lesion localization suitable for CT triage and prioritization.