Multi-scale boundary neural network for gastric tumor segmentation

At present, gastric cancer patients account for a large proportion of all tumor patients. Gastric tumor image segmentation can provide a reliable additional basis for the clinical analysis and diagnosis of gastric cancer. However, the existing gastric cancer image datasets have disadvantages such as small data sizes and difficulty in labeling. Moreover, most existing CNN-based methods are unable to generate satisfactory segmentation masks without accurate labels, which are due to the limited context information and insufficient discriminative feature maps obtained after the consecutive pooling and convolution operations. This paper presents a gastric cancer lesion dataset for gastric tumor image segmentation research. A multiscale boundary neural network (MBNet) is proposed to automatically segment the real tumor area in gastric cancer images. MBNet adopts encoder–decoder architecture. In each stage of the encoder, a boundary extraction refinement module is proposed for obtaining multi granular edge information and refinement firstly. Then, we build a selective fusion module to selectively fuse features from the different stages. By cascading the two modules, the richer context and fine-grained features of each stage are encoded. Finally, the astrous spatial pyramid pooling is improved to obtain the remote dependency relationship of the overall context and the fine spatial structure information. The experimental results show that the accuracy of the model reaches 92.3%, the similarity coefficient (DICE) reaches 86.9%, and the performance of the proposed method on the CVC-ClinicDB and Kvasir-SEG datasets also outperforms existing approaches.