Abstract—Drawing support from an effective Medical Image Segmentation (MIS) is conducive to a substantial diagnostic basis for the physicians to identify the focus lesion in the patient body and give the subsequent clinical assessment of the patient status.Although various works have tried the challenging quantitative analysis problem, it is still diffificult to conduct precise automatic segmentation, especially the soft tissue organs. In this decade,with the increased amount of available datasets, deep learning based networks have achieved remarkable performance in image processing. Inspired by the state-of-the-art deep learning works,in this paper, we propose an end-to-end multi-layer network named RCGA-Net. It consists of an encoder-decoder backbone that integrates a coordinate attention mechanism based on space and channel and a global context extraction module to highlight more valuable information. To evaluate the performance of RCGA-Net, we apply it to different kinds of clinical and exper imental MIS tasks to testify its generalization ability. Extensive experiments represent that our schema has taken the outperform result among the comparison methods group.