Improved Complementary Pulmonary Nodule Segmentation Model Based on Multi-Feature Fusion

Abstract

Accurate segmentation of lung nodules from pulmonary computed tomography (CT) slices plays a vital role in the analysis and diagnosis of lung cancer. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in the automatic segmentation of lung nodules. However, they are still challenged by the large diversity of segmentation targets, and the small inter-class variances between the nodule and its surrounding tissues. To tackle this issue, we propose a features complementary network according to the process of clinical diagnosis, which made full use of the complementarity and facilitation among lung nodule location information, global coarse area, and edge information. Specifically, we first consider the importance of global features of nodules in segmentation and propose a cross-scale weighted high-level feature decoder module. Then, we develop a low-level feature decoder module for edge feature refinement. Finally, we construct a complementary module to make information complement and promote each other. Furthermore, we weight pixels located at the nodule edge on the loss function and add an edge supervision to the deep supervision, both of which emphasize the importance of edges in segmentation. The experimental results demonstrate that our model achieves robust pulmonary nodule segmentation and more accurate edge segmentation.

An improved V-Net lung nodule segmentation model based on pixel threshold separation and attention mechanism

“…Hou et al 17 , proposed a model named MSR-UNet, which is based on U-Net network model and integrates self-attention, multi-scale features and residual structure, and achieves a good Dice coefficient of 91.87% and an IoU of 86.8% on LIDC dataset. Tang et al 18 proposed a Res2Net50-based backbone network, which includes three modules : High-Level Feature Decoder Module (HDM), Low-Level Feature Decoder Module (LDM) and Complementary Module (CM). Through the recognition and fusion of high-level and low-level semantic information, more accurate edge segmentation is achieved.…”