In the management of lung nodules, we are desirable to predict nodule evolution in terms of its diameter variation on Computed Tomography (CT) scans and then provide a follow-up recommendation according to the predicted result of the growing trend of the nodule. In order to improve the performance of growth trend prediction for lung nodules, it is vital to compare the changes of the same nodule in consecutive CT scans. Motivated by this, we screened out 4,666 subjects with more than two consecutive CT scans from the National Lung Screening Trial (NLST) dataset to organize a temporal dataset called NLSTt. In specific, we first detect and pair regions of interest (ROIs) covering the same nodule based on registered CT scans. After that, we predict the texture category and diameter size of the nodules through models. Last, we annotate the evolution class of each nodule according to its changes in diameter. Based on the built NLSTt dataset, we propose a siamese encoder to simultaneously exploit the discriminative features of 3D ROIs detected from consecutive CT scans. Then we novelly design a spatial-temporal mixer (STM) to leverage the interval changes of the same nodule in sequential 3D ROIs and capture spatial dependencies of nodule regions and the current 3D ROI. According to the clinical diagnosis routine, we employ hierarchical loss to pay more attention to growing nodules. The extensive experiments on our organized dataset demonstrate the advantage of our proposed method. We also conduct experiments on an in-house dataset to evaluate the clinical utility of our method by comparing it against skilled clinicians.
翻译:在管理肺结核方面,我们最好预测结核在测算表扫描(CT)的直径变异方面的直径变化,然后根据结核增长趋势的预测结果提出后续建议。为了改进肺结核增长趋势预测的性能,必须比较连续的CT扫描中同一结核的变化。我们为此从全国肺癌筛选试验(NLST)数据集中连续两次扫描4 666个主题,以组织一个名为NLSTt的时间数据集。具体地说,我们首先根据登记的CT扫描,探测和配对有兴趣的结核实验区域(ROIs),覆盖同一结核的预测结果。此后,我们通过模型预测结核的生长趋势趋势趋势值类别和直径大小。最后,我们根据每一结核的直径变化来说明每个结核的进化等级。根据建造的NLSTt数据集,我们建议用一个Siamse concenter 来同时利用从连续CT扫描中测得的3D ROIs的辨别特征。我们首先检测和配对基于已注册的常规运行过程(ROTM)的不断升级的周期数据分析方法进行对比。然后,我们重新设计一个空间-BILS 和不断分析的周期周期周期分析的比重的计算方法,然后将一个比比重数据转换一个空间-BILILM的计算。