Positron emission tomography (PET)-Computed tomography (CT) plays an important role in
cancer management. As a multi-modal imaging technique it provides both functional and anatomical
information of tumor spread. Such information improves cancer treatment in many ways. One
important usage of PET-CT in cancer treatment is to facilitate radiotherapy planning, for the information
it provides helps radiation oncologists to better target the tumor region. However, currently
most tumor delineations in radiotherapy planning are performed by manual segmentation, which
consumes a lot of time and work. Most computer-aided algorithms need a knowledgeable user to
locate roughly the tumor area as a starting point. This is because, in PET-CT imaging, some tissues
like heart and kidney may also exhibit a high level of activity similar to that of a tumor region. In
order to address this issue, a novel co-segmentation method is proposed in this work to enhance
the accuracy of tumor segmentation using PET-CT, and a localization algorithm is developed to
differentiate and segment tumor regions from normal regions. On a combined dataset containing
29 patients with lung tumor, the combined method shows good segmentation results as well as
good tumor recognition rate
