A Method for PET-CT Lung Cancer Segmentation based on Improved Random Walk

Abstract

Segmentation methods only work for a single imaging modality usually suffer from the low spatial resolution in positron emission tomography (PET) or low contrast in computed tomography (CT) when the tumor region is inhomogeneous or not obvious. To address this problem, we develop a segmentation method combining the advantages and disadvantages of PET and CT. Firstly, the initial contours are obtained by the pre-segmentation of PET images using region growing and mathematical morphology. The initial contours can be used to automatically obtain the seed points required for random walk on PET and CT images, at the same time, they can be also used as a constraint in the random walk on CT images to solve the shortcoming that the tumor areas are not obvious if the CT images have not been enhanced. For the reason that CT provides essential details on anatomic structures, the anatomic structures of CT can be used to improve the weight of random walk on PET images. Finally, the similarity matrices obtained by random walk on PET and CT images are weighted to obtain identical results on PET and CT images. Our methods achieve an average DSC of $\pmb{0.8456\pm} \pmb{0.0703}$ on 14 patients with lung cancer. Our method has much better performance when the tumors are inhomogeneous on PET images and not obvious on CT images.