Translator Disclaimer
17 March 2008 CT-guided automated detection of lung tumors on PET images
Author Affiliations +
The calculation of standardized uptake values (SUVs) in tumors on serial [18F]2-fluoro-2-deoxy-D-glucose (18F-FDG) positron emission tomography (PET) images is often used for the assessment of therapy response. We present a computerized method that automatically detects lung tumors on 18F-FDG PET/Computed Tomography (CT) images using both anatomic and metabolic information. First, on CT images, relevant organs, including lung, bone, liver and spleen, are automatically identified and segmented based on their locations and intensity distributions. Hot spots (SUV >= 1.5) on 18F-FDG PET images are then labeled using the connected component analysis. The resultant "hot objects" (geometrically connected hot spots in three dimensions) that fall into, reside at the edges or are in the vicinity of the lungs are considered as tumor candidates. To determine true lesions, further analyses are conducted, including reduction of tumor candidates by the masking out of hot objects within CT-determined normal organs, and analysis of candidate tumors' locations, intensity distributions and shapes on both CT and PET. The method was applied to 18F-FDG-PET/CT scans from 9 patients, on which 31 target lesions had been identified by a nuclear medicine radiologist during a Phase II lung cancer clinical trial. Out of 31 target lesions, 30 (97%) were detected by the computer method. However, sensitivity and specificity were not estimated because not all lesions had been marked up in the clinical trial. The method effectively excluded the hot spots caused by mediastinum, liver, spleen, skeletal muscle and bone metastasis.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yunfeng Cui, Binsheng Zhao, Timothy J. Akhurst, Jiayong Yan, and Lawrence H. Schwartz "CT-guided automated detection of lung tumors on PET images", Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69152N (17 March 2008);

Back to Top