Generative inpainting-based anomaly detection for CT liver tumor detection Journal Article


Authors: Shi, Y.; Niu, C.; Simpson, A. L.; Man, B. D.; Do, R.; Wang, G.
Article Title: Generative inpainting-based anomaly detection for CT liver tumor detection
Abstract: 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. © 2025 IEEE.
Keywords: image enhancement; computerized tomography; liver tumor; liver disease; ct; liver tumors; reconstructed image; anomaly detection; diffusion prior; anomaly detection methods; inpainting; method analysis; tumour detection
Journal Title: IEEE Transactions on Radiation and Plasma Medical Sciences
ISSN: 2469-7311
Publisher: IEEE  
Publication status: Online ahead of print
Date Published: 2025-03-17
Online Publication Date: 2025-03-17
Language: English
DOI: 10.1109/trpms.2025.3551946
PROVIDER: scopus
DOI/URL:
Notes: Source: Scopus
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  1. Kinh Gian Do
    260 Do