D2E2-Net: Double Deep Edge Enhancement for Weakly-Supervised Cell Nuclei Segmentation with Incomplete Point Annotations

Cell nuclei segmentation is important for histopathology image analysis. While deep learning has demonstrated promising results for automated cell nuclei segmentation, it is difficult to obtain accurate ground truth annotations due to the visual complexity of histopathology images and high density of cells. Weakly supervised cell segmentation can greatly reduce the effort required for annotation while maintaining high accuracy. However, current weakly supervised segmentation methods typically require the annotation of centroids for all cells, which is still a tedious task. In our study, we propose a semi- and weakly-supervised cell segmentation network named Deep Double Edge Enhancement Network (D2E2-Net) using only a small amount of points annotated. Our method focuses on tackling the issue of denoising the background noise to further enhance the cell boundary delineation. Our experimental results demonstrate state-of-the-art performance on three public histopathology image datasets.