Automated detection and delineation of lung tumors in PET-CT volumes using a lung atlas and iterative mean-SUV threshold

Medical Imaging 2009 : Image Processing, Lake Buena Vista, U.S.A., February 2009Automated segmentation for the delineation of lung tumors with PET-CT is a challenging task. In PET images, primary lung tumors can have varying degrees of tracer uptake, which sometimes does not differ markedly from normal adjacent structures such as the mediastinum, heart and liver. In addition, separation of tumor from adjacent soft tissues and bone in the chest wall is problematic due to limited resolution. For CT, the tumor soft tissue density can be similar to that in the blood vessels and the chest wall; and although CT provides better boundary definition, exact tumor delineation is also difficult when the tumor density is similar to adjacent structures. We propose an innovative automated adaptive method to delineate lung tumors in PET-CT images in conjunction with a lung atlas in which an iterative mean-SUV (Standardized Uptake Value) threshold is used to gradually define the tumor region in PET. Tumor delineation in the CT data is performed using region growing and seeds obtained autonomously from the PET tumor regions. We evaluated our approach in 13 patients with non-small cell lung cancer (NSCLC) and found it could delineate tumors of different size, shape and location, even when when the NSCLC involved the chest wall.Department of Electronic and Information EngineeringRefereed conference pape