Segmentation of pulmonary nodules in CT images based on 3D‐UNET combined with three‐dimensional conditional random field optimization

• Published in: Medical physics (Lancaster) Vol. 47; no. 9; pp. 4054 - 4063
• Main Authors: Wu, Wenhao, Gao, Lei, Duan, Huihong, Huang, Gang, Ye, Xiaodan, Nie, Shengdong
• Format: Journal Article
• Language: English
• Published: United States 01.09.2020
• Subjects: 3D‐CRF; 3D‐UNET; Diagnosis, Computer-Assisted; Humans; Lung - diagnostic imaging; Lung Neoplasms - diagnostic imaging; Multiple Pulmonary Nodules - diagnostic imaging; pulmonary nodule segmentation; Radiographic Image Interpretation, Computer-Assisted; Solitary Pulmonary Nodule - diagnostic imaging; Tomography, X-Ray Computed; 3D-CRF; CT; 3D-UNET; pulmonary nodule segmentation
• ISSN: 0094-2405; 2473-4209; 2473-4209
• DOI: 10.1002/mp.14248

Purpose 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. Method 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)1 database to train and test the model. What’s more, we used clinical data from partner hospitals for additional validation. Results and conclusions 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).