DMFF-Net: A dual encoding multiscale feature fusion network for ovarian tumor segmentation

Ovarian cancer is a serious threat to the female reproductive system. Precise segmentation of the tumor area helps the doctors to further diagnose the disease. Automatic segmentation techniques for abstracting high-quality features from images through autonomous learning of model have become a hot research topic nowadays. However, the existing methods still have the problem of poor segmentation of ovarian tumor details. To cope with this problem, a dual encoding based multiscale feature fusion network (DMFF-Net) is proposed for ovarian tumor segmentation. Firstly, a dual encoding method is proposed to extract diverse features. These two encoding paths are composed of residual blocks and single dense aggregation blocks, respectively. Secondly, a multiscale feature fusion block is proposed to generate more advanced features. This block constructs feature fusion between two encoding paths to alleviate the feature loss during deep extraction and further increase the information content of the features. Finally, coordinate attention is added to the decoding stage after the feature concatenation, which enables the decoding stage to capture the valid information accurately. The test results show that the proposed method outperforms existing medical image segmentation algorithms for segmenting lesion details. Moreover, the proposed method also performs well in two other segmentation tasks.