Globally Optimal Tumor Segmentation in PET-CT Images: A Graph-Based Co-segmentation Method

Tumor segmentation in PET and CT images is notoriously challenging due to the low spatial resolution in PET and low contrast in CT images. In this paper, we proposed a general framework to use both PET and CT images simultaneously for tumor segmentation. Our method utilizes the strength of each imaging modality: the superior contrast of PET and the superior spatial resolution of CT. We formulated this problem as a Markov Random Field (MRF) based segmentation of the image pair with a regularized term that penalizes the segmentation difference between PET and CT. Our method simulates the clinical practice of delineating tumor simultaneously using both PET and CT, and is able to concurrently segment tumor from both modalities, achieving globally optimal solutions in low-order polynomial time by a single maximum flow computation. The method was evaluated on clinically relevant tumor segmentation problems. The results showed that our method can effectively make use of both PET and CT ima...

• Type: Journal
• Year: 2011
• Where: IPMI
• Authors: Dongfeng Han, John E. Bayouth, Qi Song, Aakant Taurani, Milan Sonka, John M. Buatti, Xiaodong Wu
• Keywords: IPMI 2011, Medical Imaging, Polynomial Time, Segmentation Problems, Tumor Segmentation