Feature Space Message Passing Network for Medical Image Semantic Segmentation

Abstract

Accurate semantic segmentation of medical images is of significant importance for subsequent processing and analysis. The encoder-decoder deep learning framework has been widely applied for numerous medical image segmentation tasks. However, most existing approaches are restricted by the limited receptive field for failing to capture longrange dependencies, meanwhile lacking global features for spatial information recovery. To solve both problems, we propose a novel feature space message passing network (FSMPN) framework. At first, a dynamic message passing block (DMPB) is proposed to perform the long-range interactions for better feature learning between voxels. Secondly, a skipped graph connection (SGC) module is developed to explicitly transfer learned graph with features from encoder stage to decoder stage to help recover spatial information. The proposed FSMPN was able to achieve superior performance on different types of medical image datasets compared to other popular models.