Point annotations for nucleus segmentation in histopathology images via envelope enhancement network

Abstract

Nucleus segmentation in histopathology images holds great clinical value for disease analysis. With the advancements in deep learning and continuous iteration of devices, existing nucleus segmentation algorithms can achieve satisfactory performance. However, these fully supervised nucleus segmentation methods require pixel-level annotation information, which requires a significant amount of time and effort. To ease this burden of annotations, we propose an envelope enhancement segmentation Network (EESNet) for weak supervison nucleus segmentation. Specifically, we first employ a Voronoi diagram and adaptive k-means clustering algorithm to derive two kinds of informative pixel-level pseudo labels,which are used for pixel-wise segmentation. Furthermore, we introduce an envelope enhancement network to provide rich envelope structure information to the segmentation network, which helps mitigate the problem of inaccurate boundary segmentation caused by point annotations. Finally, our network is verified on a mainstream nucleus segmentation datasets (consep) and achieves satisfactory performance.