Mask Guidance Pyramid Network for Overlapping Cervical Cell Edge Detection

An important indicator of cervical cancer diagnosis is to calculate the proportion of diseased cells and cancer cells, so it is necessary to segment cells and judge the cell status. The existing methods are difficult to deal with the segmentation of overlapping cells. In order to solve this problem, we put forward such a hypothesis by reading a large number of literature, that is, image segmentation and edge measurement tasks have unity in high-level features. To prove this hypothesis, in this paper, we focus on the complementary between overlapping cervical cell edge information and cell object information to get higher accuracy cell edge detection results. Specifically, we present a joint multi-task learning framework for overlapping cell edge detection by the mask guidance pyramid network. The main component of the framework is the Mask Guidance Module (MGM), which integrates two tasks and stores the shared latent semantics to interact in the two tasks. For semantic edge detection, we propose the novel Refinement Aggregated Module (RAM) fusion to promote semantic edges. Finally, to improve the edge pixel accuracy, the edge consistency constraint loss function is introduced to our model training. Our extensive experiments have proved that our method outperforms other edge detection efforts.