Dual‐branch feature fusion S3D V‐Net network for lung nodules segmentation

Abstract

AbstractBackgroundAccurate segmentation of lung nodules can help doctors get more accurate results and protocols in early lung cancer diagnosis and treatment planning, so that patients can be better detected and treated at an early stage, and the mortality rate of lung cancer can be reduced.PurposeCurrently, the improvement of lung nodule segmentation accuracy has been limited by his heterogeneous performance in the lungs, the imbalance between segmentation targets and background pixels, and other factors. We propose a new 2.5D lung nodule segmentation network model for lung nodule segmentation. This network model can well improve the extraction of edge information of lung nodules, and fuses intra‐slice and inter‐slice features, which makes good use of the three‐dimensional structural information of lung nodules and can more effectively improve the accuracy of lung nodule segmentation.MethodsOur approach is based on a typical encoding‐decoding network structure for improvement. The improved model captures the features of multiple nodules in both 3‐D and 2‐D CT images, complements the information of the segmentation target's features and enhances the texture features at the edges of the pulmonary nodules through the dual‐branch feature fusion module (DFFM) and the reverse attention context module (RACM), and employs central pooling instead of the maximal pooling operation, which is used to preserve the features around the target and to eliminate the edge‐irrelevant features, to further improve the performance of the segmentation of the pulmonary nodules.ResultsWe evaluated this method on a wide range of 1186 nodules from the LUNA16 dataset, and averaging the results of ten cross‐validated, the proposed method achieved the mean dice similarity coefficient (mDSC) of 84.57%, the mean overlapping error (mOE) of 18.73% and average processing of a case is about 2.07 s. Moreover, our results were compared with inter‐radiologist agreement on the LUNA16 dataset, and the average difference was 0.74%.ConclusionThe experimental results show that our method improves the accuracy of pulmonary nodules segmentation and also takes less time than more 3‐D segmentation methods in terms of time.