Automatic Pulmonary Nodule Detection and Localization from Biplanar Chest Radiographs Using Convolutional Neural Network

Abstract

Chest x-ray (CXR) is the most common examination in pulmonary nodule detection and an automatic nodule detection algorithm is desirable. Currently, convolutional neural network (CNN) is widely applied in CXR. However, there is a lack of dataset with clear nodule annotation, also the small size of pulmonary nodules hampers its performance, finally, there is no study of lung nodule detection utilizing end-to-end CNN model and lateral CXR images. In this study, coronal and lateral CXR images were generated from CT phantom for training separately, and U-Net architecture CNN models were implemented with modifying a number of convolutional layers, adding shortcut connection, using weighted loss function and the impact of these modifications was evaluated on model performance. Finally, the models were tested on a test set under the condition of different nodule diameter, number, and location. In CT phantom dataset, U-Net trained with the residual unit and weighted loss showed the capability in detecting 5 mm nodules and increased training speed. Overall, model trained with coronal images provided better detection result than using lateral images, but their outputs could be combined to obtain nodule localization information in 3D. The number of nodules and adjacency of nodules has no prominent effect on detection, however, models were prone to failure when the nodule was too small (< 5 mm), was close to the edges of the lung, or was overlapped with moderate to the high-density anatomic structure.