  An accurate segmentation of lung nodules in computed tomography (CT) images
is critical to lung cancer analysis and diagnosis. However, due to the variety
of lung nodules and the similarity of visual characteristics between nodules
and their surroundings, a robust segmentation of nodules becomes a challenging
problem. In this study, we propose the Dual-branch Residual Network (DB-ResNet)
which is a data-driven model. Our approach integrates two new schemes to
improve the generalization capability of the model: 1) the proposed model can
simultaneously capture multi-view and multi-scale features of different nodules
in CT images; 2) we combine the features of the intensity and the convolution
neural networks (CNN). We propose a pooling method, called the central
intensity-pooling layer (CIP), to extract the intensity features of the center
voxel of the block, and then use the CNN to obtain the convolutional features
of the center voxel of the block. In addition, we designed a weighted sampling
strategy based on the boundary of nodules for the selection of those voxels
using the weighting score, to increase the accuracy of the model. The proposed
method has been extensively evaluated on the LIDC dataset containing 986
nodules. Experimental results show that the DB-ResNet achieves superior
segmentation performance with an average dice score of 82.74% on the dataset.
Moreover, we compared our results with those of four radiologists on the same
dataset. The comparison showed that our average dice score was 0.49% higher
than that of human experts. This proves that our proposed method is as good as
the experienced radiologist.
