Automatic segmentation of pulmonary lobes on low-dose computed tomography using deep learning

Background: To develop and validate a fully automated deep learning-based segmentation algorithm to segment pulmonary lobe on low-dose computed tomography (LDCT) images.

Methods: This study presents an automatic segmentation of pulmonary lobes using a fully convolutional neural network named dense V-network (DenseVNet) on lung cancer screening LDCT images. A total of 160 LDCT cases for lung cancer screening (100 in the training set, 10 in the validation set, and 50 in the test set) was included in this study. Specifically, the template of pulmonary lobes (the right lung consists of three lobes, and the left lung consists of two lobes) were obtained from pixel-level annotations by semiautomatic segmentation platform. Then, the model was trained under the supervision of the LDCT training set. Finally, the trained model was used to segment the LDCT in the test set. Dice coefficient, Jaccard coefficient, and Hausdorff distance were adopted as evaluation metrics to verify the performance of our segmentation model.

Results: In this study, the model achieved the accurate segmentation of each pulmonary lobe in seconds without the intervention of researchers. The testing set consisted 50 LDCT cases were used to evaluate the performance of the segmentation model. The all-lobes Dice coefficient of the test set was 0.944, the Jaccard coefficient was 0.896, and the Hausdorff distance was 92.908 mm.

Conclusions: The segmentation model based on LDCT can automatically and robustly and efficiently segment pulmonary lobes. It will provide effective location information and contour constraints for pulmonary nodule detection on LDCT images for lung cancer screening, which may have potential clinical application.