Generative Inpainting-Based Anomaly Detection for CT Liver Tumor Detection
Yongyi Shi1, Chuang Niu1, Amber L Simpson2
1Biomedical Imaging Center, Department of Biomedical Engineering, School of Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180 USA.
IEEE Transactions on Radiation and Plasma Medical Sciences
|November 14, 2025
Summary
This study introduces a novel generative diffusion prior method for improved liver disease detection using CT scans. The approach enhances anomaly detection accuracy by inpainting abnormal regions, outperforming current state-of-the-art methods.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Radiology
Background:
- Computed Tomography (CT) is crucial for diagnosing liver diseases and tumors.
- Traditional anomaly detection in CT images can miss subtle tissue differences.
- Existing methods may yield suboptimal results in identifying pathological structures.
Purpose of the Study:
- To develop an advanced anomaly detection technique for liver CT imaging.
- To leverage generative diffusion models for improved accuracy in liver disease diagnosis.
- To refine the localization and detection of liver tumors using AI.
Main Methods:
- Employing a generative diffusion prior to inpaint liver CT images.
- Utilizing an adaptive threshold for abnormal region masking.
- Calculating anomaly scores based on image discrepancies post-inpainting.
Main Results:
- Demonstrated significant improvement in liver anomaly detection accuracy.
- Achieved a 7.9% increase in the area under the curve (AUC) compared to existing methods.
- Validated the methodology on two distinct liver CT datasets.
Conclusions:
- The generative diffusion prior approach enhances radiological assessment of liver diseases.
- This AI-driven method offers superior performance in detecting liver abnormalities.
- The technique shows promise for more precise and reliable liver tumor detection.
Related Concept Videos
01:28Imaging Studies for Cardiovascular System V: CT
Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
01:27Imaging Studies III: Computed Tomography
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...