Ms. Nan Qiu Profile

Nan Qiu

at Hohai Univ

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Publications (1)

Proceedings Article | 28 October 2021 Paper

PET-CT images co-segmentation of lung tumor using joint level set model

Zhe Chen, Nan Qiu, Dongfang Dai, Xia He

Proceedings Volume 11884, 1188411 (2021) https://doi.org/10.1117/12.2605094

KEYWORDS: Image segmentation, Positron emission tomography, Computed tomography, Image fusion, Fusion energy, Tissues, Lung cancer

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Medical imaging, used for both diagnosis and therapy planning, is evolving towards multi-modality acquisition protocols. Manual segmentation of 3D images is a tedious task and prone to inter- and inter-experts variability. Moreover, the automatic segmentation exploiting the characteristics of multi-modal images is still a difficult problem. Towards this end, Positron emission tomography (PET) and computed tomography (CT) are widely used. PET imaging has a high contrast but often leads to blurry tumor edges due to its limited spatial resolution, while CT imaging has a high resolution but a low contrast between a tumor and its surrounding normal soft tissues. Tumor segmentation from either a single PET or CT image is difficult. It is known that co-segmentation methods utilizing the complementary information between PET and CT can improve the segmentation accuracy. This complementary information can be either consistent or inconsistent in the image level. How to correctly localize tumor edges with the inconsistent information is one major challenge for co-segmentation. Aiming to solve this problem, a novel joint level set model is proposed to combine the evidences of PET and CT in a united energy form, achieving a co-segmentation in these two modalities. The convergence of the co- segmentation model corresponds to the most optimal tradeoff between the PET and CT. The different characteristics in these two imaging modalities are considered in the adaptive convergence process which starts mostly with the PET evidence to constrain the tumor location and stops mostly with the CT evidences to delineate boundary details. The adaptability of our proposed model is automatically realized by stepwise moderating the joint weights during the convergence process. The performance of the proposed model is validated on 20 nonsmall cell lung tumor PET-CT images. It achieves an average dice similarity coefficient (DSC) of 0.846±0.064 and positive predictive value (PPV) of 0.889±0.079, demonstrating the high accuracy of the proposed model for PET-CT images lung tumor co-segmentation.

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