Can I read PET-CT image Co-segmentation of lung tumor using joint level set model on EtoBox?

PET-CT image Co-segmentation of lung tumor using joint level set model by Zhe Chen; Nan Qiu; Hui Feng; Dongfang Dai is a Computer Science article available to read on EtoBox.

What is PET-CT image Co-segmentation of lung tumor using joint level set model about?

Accurate lung tumor segmentation plays an important role in radiotherapy and targeted therapy. Positron emission tomography (PET) and computed tomography (CT) scanner imaging provide complementary evidence for lung tumor segmentation. In specific, PET can recognize the tumor tissues, while the tissue boundaries are blurred in such a modality. By contrast, CT has a better resolution but a lower contrast between tumor and normal tissues. It is well known that jointly exploiting the evidence from PET and CT images significantly benefits lung tumor delineation. A novel joint level set model is proposed in this paper to integrate PET and CT evidence in a unified energy form, providing co-segmentation results. The convergence result of our co-segmentation model can find the optimal tradeoff between PET and CT modalities. Different characteristics of these two modalities are comprehensively considered in the adaptive convergence process which starts mostly with the PET evidence to locate tumor tissues and stops mostly with the CT evidence to delineate tissue boundaries. The novelty of our joint level set model lies in its adaptability which stepwise moderates joint weights during the mode

Who reads PET-CT image Co-segmentation of lung tumor using joint level set model?

Author

Zhe Chen; Nan Qiu; Hui Feng; Dongfang Dai

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)