  Whole-body PET/CT scan is an important tool for diagnosing various
malignancies (e.g., malignant melanoma, lymphoma, or lung cancer), and accurate
segmentation of tumors is a key part for subsequent treatment. In recent years,
CNN-based segmentation methods have been extensively investigated. However,
these methods often give inaccurate segmentation results, such as
over-segmentation and under-segmentation. Therefore, to address such issues, we
propose a post-processing method based on a graph convolutional neural network
(GCN) to refine inaccurate segmentation parts and improve the overall
segmentation accuracy. Firstly, nnUNet is used as an initial segmentation
framework, and the uncertainty in the segmentation results is analyzed.
Certainty and uncertainty nodes establish the nodes of a graph neural network.
Each node and its 6 neighbors form an edge, and 32 nodes are randomly selected
for uncertain nodes to form edges. The highly uncertain nodes are taken as the
subsequent refinement targets. Secondly, the nnUNet result of the certainty
nodes is used as label to form a semi-supervised graph network problem, and the
uncertainty part is optimized through training the GCN network to improve the
segmentation performance. This describes our proposed nnUNet-GCN segmentation
framework. We perform tumor segmentation experiments on the PET/CT dataset in
the MICCIA2022 autoPET challenge. Among them, 30 cases are randomly selected
for testing, and the experimental results show that the false positive rate is
effectively reduced with nnUNet-GCN refinement. In quantitative analysis, there
is an improvement of 2.12 % on the average Dice score, 6.34 on 95 % Hausdorff
Distance (HD95), and 1.72 on average symmetric surface distance (ASSD). The
quantitative and qualitative evaluation results show that GCN post-processing
methods can effectively improve tumor segmentation performance.
