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.

翻译：全身PET/CT扫描是诊断多种恶性肿瘤（如恶性黑色素瘤、淋巴瘤或肺癌）的重要工具，而肿瘤的精确分割是后续治疗的关键环节。近年来，基于CNN的分割方法已被广泛研究，但这些方法常导致过分割或欠分割等不精确结果。为此，我们提出基于图卷积神经网络（GCN）的后处理方法，以精化不准确的分割区域，提升整体分割精度。首先，采用nnUNet作为初始分割框架，分析分割结果中的不确定性。确定性与不确定性节点构成图神经网络的节点：每个节点与其6个邻域节点形成边，并对不确定性节点随机选取32个节点构建边，将高不确定性节点作为后续精化目标。其次，以确定性节点的nnUNet分割结果作为标签，构建半监督图网络问题，通过训练GCN网络优化不确定性部分以改善分割性能。本文提出的nnUNet-GCN分割框架在MICCIA2022 autoPET挑战赛的PET/CT数据集上进行肿瘤分割实验，其中随机选取30例进行测试。实验结果表明，经nnUNet-GCN精化后，假阳性率有效降低；定量分析中，平均Dice系数提升2.12%，95%豪斯多夫距离（HD95）改善6.34，平均对称表面距离（ASSD）提升1.72。定性与定量评估结果证明，GCN后处理方法可有效提升肿瘤分割性能。