Co-Net: A Collaborative Region-Contour-Driven Network for Fine-to-Finer Medical Image Segmentation

Anran Liu, Xiangsheng Huang, Tong Li, Pengcheng Ma; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022, pp. 1046-1055

In this paper, a fine-to-finer segmentation task is investigated driven by region and contour features collaboratively on Glomerular Electron-Dense Deposits (GEDD) in view of the complementary nature of these two types of features. To this end, a novel network (Co-Net) is presented to dynamically use fine saliency segmentation to guide finer segmentation on boundaries. The whole architecture contains double mutually boosted decoders sharing one common encoder. Specifically, a new structure named Global-guided Interaction Module (GIM) is designed to effectively control the information flow and reduce redundancy in the cross-level feature fusion process. At the same time, the global features are used in it to make the features of each layer gain access to richer context, and a fine segmentation map is obtained initially; Discontinuous Boundary Supervision (DBS) strategy is applied to pay more attention to discontinuity positions and modifying segmentation errors on boundaries. At last, Selective Kernel (SK) is used for dynamical aggregation of the region and contour features to obtain a finer segmentation. Our proposed approach is evaluated on an independent GEDD dataset labeled by pathologists and also on open polyp datasets to test the generalization. Ablation studies show the effectiveness of different modules. On all datasets, our proposal achieves high segmentation accuracy and surpasses previous methods.