Wu Wenhao, Gao Lei, Duan Huihong, Huang Gang, Ye Xiaodan, Nie Shengdong
School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, People's Republic of China.
Shanghai University of Medicine & Health Science, Shanghai, 201318, People's Republic of China.
Med Phys. 2020 Sep;47(9):4054-4063. doi: 10.1002/mp.14248. Epub 2020 Jun 4.
Pulmonary nodules are a potential manifestation of lung cancer. In computer-aided diagnosis (CAD) of lung cancer, it is of great significance to extract the complete boundary of the pulmonary nodules in the computed tomography (CT) scans accurately. It can provide doctors with important information such as tumor size and density, which assist doctors in subsequent diagnosis and treatment. In addition to this, in the molecular subtype and radiomics of lung cancer, segmentation of lung nodules also plays a pivotal role. Existing methods are difficult to use only one model to simultaneously treat the boundaries of multiple types of lung nodules in CT images.
In order to solve the problem, this paper proposed a three-dimensional (3D)-UNET network model optimized by a 3D conditional random field (3D-CRF) to segment pulmonary nodules. On the basis of 3D-UNET, the 3D-CRF is used to optimize the sample output of the training set, so as to update the network weights in training process, reduce the model training time, and reduce the loss rate of the model. We selected 936 sets of pulmonary nodule data for the lung image database consortium and image database resource initiative (LIDC-IDRI) database to train and test the model. What's more, we used clinical data from partner hospitals for additional validation.
The results show that our method is accurate and effective. Particularly, it shows more significance for the optimization of the segmentation of adhesive pulmonary nodules (the juxta-pleural and juxta-vascular nodules) and ground glass pulmonary nodules (GGNs).
肺结节是肺癌的一种潜在表现形式。在肺癌的计算机辅助诊断（CAD）中，准确提取计算机断层扫描（CT）图像中肺结节的完整边界具有重要意义。它可以为医生提供肿瘤大小和密度等重要信息，辅助医生进行后续的诊断和治疗。除此之外，在肺癌的分子亚型和影像组学中，肺结节分割也起着关键作用。现有方法很难仅用一个模型同时处理CT图像中多种类型肺结节的边界。
为了解决这个问题，本文提出了一种由三维条件随机场（3D-CRF）优化的三维（3D）-UNET网络模型来分割肺结节。在3D-UNET的基础上，利用3D-CRF对训练集的样本输出进行优化，从而在训练过程中更新网络权重，减少模型训练时间，并降低模型的损失率。我们从肺图像数据库联盟和图像数据库资源计划（LIDC-IDRI）数据库中选取了936组肺结节数据来训练和测试该模型。此外，我们还使用了合作医院的临床数据进行额外验证。
结果表明我们的方法准确有效。特别是，它对粘连性肺结节（胸膜旁和血管旁结节）和磨玻璃肺结节（GGNs）分割的优化显示出更大的意义。
