Biomedical image segmentation based on full-Resolution network

Abstract

• A new CNN structure is designed to extract two complementary features of image. • A new feature is used to remedy the missing image details in semantic feature . • A full-feature extraction module is designed to extract the new feature. • A fusion module is used to aggregate image features selectively and effectively. Convolutional neural networks (CNN) has been widely used in biomedical image segmentation (BIS) tasks for its remarkable feature representation capability, and most of existing CNN-based segmentation networks leverage a down-sampling operation to achieve larger acceptance domain. However, down-sampling operations could inevitably loss the detailed information of images which is very important for the BIS task. In this paper, we propose a full-resolution biomedical image segmentation network(FRNet) that could maintain the integrated detailed information of image while keeping sufficient semantic information and large receptive field. Specifically, the basic semantic feature and non-destructive feature are employed to represent the semantic and detailed information of images, respectively. A backbone network and a new full-feature extraction branch are conducted to extract those two kinds of complementary features. Furthermore, a novel feature fusion module is designed to integrate those complementary features to achieve non-destructive description of images. Finally, in order to further improve the description ability of the integrated feature, a Densely connected Atrous Spatial Pyramid Pooling(DenseASPP) module is arranged at the end of our proposed FRNet to extract the multiscale information of images. Thorough experimental results on several available databases demonstrate the effectiveness and advancement of FRNet.