基于图神经网络的关系学习肿瘤成像中淋巴结肿瘤体积检测
Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network
摘要：
确定GTV $_{LN}$的扩散对于许多癌症的手术切除和放射治疗的下游工作流程定义相应的切除或照射区域至关重要。与更常见的肿大淋巴结（LN）不同，GTV $_{LN}$还包括较小的淋巴结，如果与高正电子发射断层扫描信号和/或CT中的任何转移征象相关。这是一项艰巨的任务。在这项工作中，我们提出了一个统一的LN外观和LN间的关系学习框架来检测真正的GTV $_{LN}$。这是由先前的临床知识激发的，即淋巴结形成连接的淋巴系统，并且癌细胞在淋巴结中的扩散通常遵循某些途径。具体来说，我们首先利用具有ROI池的3D卷积神经网络来提取GTV $_{LN}$的实例外观特征。接下来，我们引入一个图神经网络来进一步对LN间关系进行建模，其中将全局LN-肿瘤空间先验信息包含在学习过程中。这导致端到端可训练网络通过对GTV $_{LN}$进行分类来检测。我们在一组由初步第一阶段方法生成的GTV $_{LN}$候选者上操作我们的模型，该方法具有$>85\%$的灵敏度，但代价是高假阳性（FP）（每个患者的$>15$ FP）。我们验证了我们的方法在放射治疗数据集与142对PET/RTCT扫描包含胸部和上腹部身体部位。所提出的方法显着改善了国家的最先进的（SOTA）LN分类方法$5.5\%$和$13.1\%$的F1评分和平均灵敏度值在$2, 3, 4, 6$ FP每例患者，分别。
英文摘要：
Determining the spread of GTV$_{LN}$ is essential in defining the respective resection or irradiating regions for the downstream workflows of surgical resection and radiotherapy for many cancers. Different from the more common enlarged lymph node (LN), GTV$_{LN}$ also includes smaller ones if associated with high positron emission tomography signals and/or any metastasis signs in CT. This is a daunting task. In this work, we propose a unified LN appearance and inter-LN relationship learning framework to detect the true GTV$_{LN}$. This is motivated by the prior clinical knowledge that LNs form a connected lymphatic system, and the spread of cancer cells among LNs often follows certain pathways. Specifically, we first utilize a 3D convolutional neural network with ROI-pooling to extract the GTV$_{LN}$'s instance-wise appearance features. Next, we introduce a graph neural network to further model the inter-LN relationships where the global LN-tumor spatial priors are included in the learning process. This leads to an end-to-end trainable network to detect by classifying GTV$_{LN}$. We operate our model on a set of GTV$_{LN}$ candidates generated by a preliminary 1st-stage method, which has a sensitivity of $>85\%$ at the cost of high false positive (FP) ($>15$ FPs per patient). We validate our approach on a radiotherapy dataset with 142 paired PET/RTCT scans containing the chest and upper abdominal body parts. The proposed method significantly improves over the state-of-the-art (SOTA) LN classification method by $5.5\%$ and $13.1\%$ in F1 score and the averaged sensitivity value at $2, 3, 4, 6$ FPs per patient, respectively.
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