  CT is a main modality for imaging liver diseases, valuable in detecting and
localizing liver tumors. Traditional anomaly detection methods analyze
reconstructed images to identify pathological structures. However, these
methods may produce suboptimal results, overlooking subtle differences among
various tissue types. To address this challenge, here we employ generative
diffusion prior to inpaint the liver as the reference facilitating anomaly
detection. Specifically, we use an adaptive threshold to extract a mask of
abnormal regions, which are then inpainted using a diffusion prior to
calculating an anomaly score based on the discrepancy between the original CT
image and the inpainted counterpart. Our methodology has been tested on two
liver CT datasets, demonstrating a significant improvement in detection
accuracy, with a 7.9% boost in the area under the curve (AUC) compared to the
state-of-the-art. This performance gain underscores the potential of our
approach to refine the radiological assessment of liver diseases.
