Use of anatomical priors in the segmentation of PET lung tumor images

Advances in dual-modality imaging that combine anatomical and functional images have considerably improved tumor staging and treatment planning. PET/CT can, for example, detect tumor invasion into adjacent tissues, as well as provide precise localization of lesions, even when no morphological changes are identified by CT. In lung cancer staging and therapy planning, determination of a tumor's size, its invasion into adjacent structures, mediastinal node status, and the detection of distant metastases are of great importance. In this study, we investigated the use of anatomical priors in the segmentation of tumors in simulated functional PET images of the lungs. The anatomical information was used as priors to extract the lung structures from the co-aligned PET data. The performance of a conventional iterative pixel-classification algorithm of fuzzy c-means (FCM) cluster analysis for segmenting the PET data with and without the use of the priors was quantitatively evaluated. A Monte Carlo simulation of PET with anatomical priors derived from the Zubal whole-body phantom was used in the evaluation. We demonstrate that the use of the anatomical priors to restrict the PET data to regions of interest consisting only of lung structures is able to improve the accuracy and reliability of the cluster analysis segmentation of lung tumors in PET images.