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27 March 2009 Automated detection and delineation of lung tumors in PET-CT volumes using a lung atlas and iterative mean-SUV threshold
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Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 72593F (2009) https://doi.org/10.1117/12.811494
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
Automated 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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Cherry Ballangan, Xiuying Wang, Stefan Eberl, Michael Fulham, and Dagan Feng "Automated detection and delineation of lung tumors in PET-CT volumes using a lung atlas and iterative mean-SUV threshold", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72593F (27 March 2009); https://doi.org/10.1117/12.811494
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